Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract." <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Machine techniques for reducing technical documents to their essential discriminating indices are investigated. Human scanning patterns in selecting "topic sentences" and phrases composed of nouns and modifiers were simulated by computer program. The amount of condensation resulting from each method and the relative uniformity in indices are examined. It is shown that the coordinated index provided by the phrase is the more meaningful and discriminating. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> This paper describes new methods of automatically extracting documents for screening purposes, i.e. the computer selection of sentences having the greatest potential for conveying to the reader the substance of the document. While previous work has focused on one component of sentence significance, namely, the presence of high-frequency content words (key words), the methods described here also treat three additional components: pragmatic words (cue words); title and heading words; and structural indicators (sentence location). The research has resulted in an operating system and a research methodology. The extracting system is parameterized to control and vary the influence of the above four components. The research methodology includes procedures for the compilation of the required dictionaries, the setting of the control parameters, and the comparative evaluation of the automatic extracts with manually produced extracts. The results indicate that the three newly proposed components dominate the frequency component in the production of better extracts. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> This paper describes TextTiling, an algorithm for partitioning expository texts into coherent multi-paragraph discourse units which reflect the subtopic structure of the texts. The algorithm uses domain-independent lexical frequency and distribution information to recognize the interactions of multiple simultaneous themes. Two fully-implemented versions of the algorithm are described and shown to produce segmentation that corresponds well to human judgments of the major subtopic boundaries of thirteen lengthy texts. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We have developed a Japanese full-text retrieval system named BREVIDOC* that enables the user to specify an area within a text for abstraction and to control the volume of the abstract interactively. This system analyzes a document structure using linguistic knowledge only and thus is domain-independent. In its text structure analysis, the system determines relations among paragraphs and sentences, based on linguistic clues such as connectives, anaphoric expressions, and idiomatic expressions. The system analyzes and stores the text structure in advance so that it can generate an abstract in real time by selecting sentences according to relative importance of rhetorical relations among the sentences. The retrieval system works on an engineering workstation. <s> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> ●● ● ● ● To summarize is to reduce in complexity, and hence in length, while retaining some of the essential qualities of the original. This paper focusses on document extracts, a particular kind of computed document summary. Document extracts consisting of roughly 20% of the original cart be as informative as the full text of a document, which suggests that even shorter extracts may be useful indicative summmies. The trends in our results are in agreement with those of Edmundson who used a subjectively weighted combination of features as opposed to training the feature weights using a corpus. <s> BIB006 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We present a natural language system which summarizes a series of news articles on the same event. It uses summarization operators, identified through empirical analysis of a corpus of news summaries, to group together templates from the output of the systems developed for ARPA’s Message Understanding Conferences. Depending on the available resources (e.g., space), summaries of different length can be produced. Our research also provides a methodological framework for future work on the summarization task and on the evaluation of news summarization systems. <s> BIB007 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We investigate one technique to produce a summary of an original text without requiring its full semantic interpretation, but instead relying on a model of the topic progression in the text derived from lexical chains. We present a new algorithm to compute lexical chains in a text, merging several robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger, shallow parser for the identification of nominal groups, and a segmentation algorithm. Summarization proceeds in four steps: the original text is segmented, lexical chains are constructed, strong chains are identified and significant sentences are extracted. We present in this paper empirical results on the identification of strong chains and of significant sentences. Preliminary results indicate that quality indicative summaries are produced. Pending problems are identified. Plans to address these short-comings are briefly presented. <s> BIB008 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> A gate network which permits two levels of logic to be performed by interconnected gate networks which are controlled by the same phase. <s> BIB009 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> This paper addresses the problem of identifying likely topics of texts by their position in the text. It describes the automated training and evaluation of an Optimal Position Policy, a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure. This method can be used in applications such as information retrieval, routing, and text summarization. <s> BIB010 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out. <s> BIB011 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Researchers in discourse have long hypothesized that the nuclei of a rhetorical structure tree provide a good summary of the text for which that tree was built. In this paper, I discuss a psychohnguistic experiment that validates this hypothesis, but that also shows that the distinction between nuclei and satelhtes is not sufficient if we want to build summaries of very high quality. I empirically compare various techniques for mapping discourse trees into partial orders that reflect the importance of the elementary textual units in texts and I discuss both their strengths and weaknesses. <s> BIB012 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We present a methodology for summarization of news about current events in the form of briefings that include appropriate background (historical) information. The system that we developed, SUMMONS, uses the output of systems developed for the DARPA Message Understanding Conferences to generate summaries of multiple documents on the same or related events, presenting similarities and differences, contradictions, and generalizations among sources of information. We describe the various components of the system, showing how information from multiple articles is combined, organized into a paragraph, and finally, realized as English sentences. A feature of our work is the extraction of descriptions of entities such as people and places for reuse to enhance a briefing. <s> BIB013 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> This paper presents a method for combining ::: query-relevance with information-novelty in the context ::: of text retrieval and summarization. The Maximal ::: Marginal Relevance (MMR) criterion strives to reduce ::: redundancy while maintaining query relevance in ::: re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results ::: indicate some benefits for MMR diversity ranking ::: in document retrieval and in single document summarization. ::: The latter are borne out by the recent results of the ::: SUMMAC conference in the evaluation of summarization ::: systems. However, the clearest advantage is demonstrated ::: in constructing non-redundant multi-document ::: summaries, where MMR results are clearly superior to ::: non-MMR passage selection. <s> BIB014 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> In this paper we compare performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles in order to learn how topic prominence affects the performance of each heuristic. We study how different query types can affect the performance of each heuristic and discuss the possibility of using machine learning algorithms to automatically learn good combination functions to combine several heuristics. We also briefly describe the design, implementation, and performance of a multilingual text summarization system SUMMARIST. <s> BIB015 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We present a method to automatically generate a concise summary by identifying and synthesizing similar elements across related text from a set of multiple documents. Our approach is unique in its usage of language generation to reformulate the wording of the summary. <s> BIB016 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> From the Publisher: ::: Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task.. "Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections. Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas. <s> BIB017 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> In order to produce a good summary, one has to identify the most relevant portions of a given text. We describe in this paper a method for automatically training topic signatures-sets of related words, with associated weights, organized around head topics and illustrate with signatures we created with 6,194 TREC collection texts over 4 selected topics. We describe the possible integration of topic signatures with outologies and its evaluaton on an automated text summarization system. <s> BIB018 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> This paper describes a framework for multi-document summarization which combines three premises: coherent themes can be identified reliably; highly representative themes, running across subsets of the document collection, can function as multi-document summary surrogates; and effective end-use of such themes should be facilitated by a visualization environment which clarifies the relationship between themes and documents. We present algorithms that formalize our framework, describe an implementation, and demonstrate a prototype system and interface. <s> BIB019 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Topic distillation is the analysis of hyperlink graph structure to identify mutually reinforcing authorities (popular pages) and hubs (comprehensive lists of links to authorities). Topic distillation is becoming common in Web search engines, but the best-known algorithms model the Web graph at a coarse grain, with whole pages as single nodes. Such models may lose vital details in the markup tag structure of the pages, and thus lead to a tightly linked irrelevant subgraph winning over a relatively sparse relevant subgraph, a phenomenon called topic drift or contamination . The problem gets especially severe in the face of increasingly complex pages with navigation panels and advertisement links. We present an enhanced topic distillation algorithm which analyzes text, the markup tag trees that constitute HTML pages, and hyperlinks between pages. It thereby identifies subtrees which have high text- and hyperlink-based coherence w.r.t. the query. These subtrees get preferential treatment in the mutual reinforcement process. Using over 50 queries, 28 from earlier topic distillation work, we analyzed over 700,000 pages and obtained quantitative and anecdotal evidence that the new algorithm reduces topic drift. <s> BIB020 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> The end users of medical digital libraries need quick access to information that is specific to the patients under their care. We present a summarization system that finds and extracts results from multiple medical journal articles returned by a search, filters results that match the patient and merges and orders the remaining facts for the summary. Our approach features an integration of text categorization, information extraction, information fusion and text reformulation for the summarization task. <s> BIB021 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. <s> BIB022 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. <s> BIB023 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> We explore a semantic abstraction approach to automatic summarization in the biomedical domain. The approach relies on a semantic processor that functions as the source interpreter and produces a list of predications. A transformation stage then generalizes and condenses this list, ultimately generating a conceptual condensate for a disorder input topic. The final condensate is displayed in graphical form. We provide a set of principles for the transformation stage and describe the application of this approach to multidocument input. Finally, we examine the characteristics and quality of the condensates produced. <s> BIB024 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> The problem of using topic representations for multi-document summarization (MDS) has received considerable attention recently. In this paper, we describe five different topic representations and introduce a novel representation of topics based on topic themes. We present eight different methods of generating MDS and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations. <s> BIB025 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents. Sentence fusion involves bottom-up local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources. <s> BIB026 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Automatic document summarization is a problem of creating a document surrogate that adequately represents the full document content. We aim at a summarization system that can replicate the quality of summaries created by humans. In this paper we investigate the machine learning method for extracting full sentences from documents based on the document semantic graph structure. In particular, we explore how the Support Vector Machines (SVM) learning method is affected by the quality of linguistic analyses and the corresponding semantic graph representations. We apply two types of linguistic analysis: (1) a simple part-of-speech tagging of noun phrases and verbs and (2) full logical form analysis which identifies Subject-Predicate-Object triples, and then build the semantic graphs. We train the SVM classifier to identify summary nodes and use these nodes to extract sentences. Experiments with the DUC 2002 and CAST datasets show that the SVM based extraction of sentences does not differ significantly for the simple and the sophisticated syntactic analysis. In both cases the graph attributes used in learning are essential for the classifier performance and the quality of extracted summaries. <s> BIB027 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> Text summarization is a method for data reduction. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the high-volume of publications. This paper presents two independent methods (BioChain and FreqDist) for identifying salient sentences in biomedical texts using concepts derived from domain-specific resources. Our semantic-based method (BioChain) is effective at identifying thematic sentences, while our frequency-distribution method (FreqDist) removes information redundancy. The two methods are then combined to form a hybrid method (ChainFreq). An evaluation of each method is performed using the ROUGE system to compare system-generated summaries against a set of manually-generated summaries. The BioChain and FreqDist methods outperform some common summarization systems, while the ChainFreq method improves upon the base approaches. Our work shows that the best performance is achieved when the two methods are combined. The paper also presents a brief physician's evaluation of three randomly-selected papers from an evaluation corpus to show that the author's abstract does not always reflect the entire contents of the full-text. <s> BIB028 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> In this paper, we introduce and compare between two novel approaches, supervised and unsupervised, for identifying the keywords to be used in extractive summarization of text documents. Both our approaches are based on the graph-based syntactic representation of text and web documents, which enhances the traditional vector-space model by taking into account some structural document features. In the supervised approach, we train classification algorithms on a summarized collection of documents with the purpose of inducing a keyword identification model. In the unsupervised approach, we run the HITS algorithm on document graphs under the assumption that the top-ranked nodes should represent the document keywords. Our experiments on a collection of benchmark summaries show that given a set of summarized training documents, the supervised classification provides the highest keyword identification accuracy, while the highest F-measure is reached with a simple degree-based ranking. In addition, it is sufficient to perform only the first iteration of HITS rather than running it to its convergence. <s> BIB029 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Approaches to Summarisation <s> In this paper, we investigate an approach for creating a comprehensive textual overview of a subject composed of information drawn from the Internet. We use the high-level structure of human-authored texts to automatically induce a domain-specific template for the topic structure of a new overview. The algorithmic innovation of our work is a method to learn topic-specific extractors for content selection jointly for the entire template. We augment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global coherence across the entire overview. The results of our evaluation confirm the benefits of incorporating structural information into the content selection process. <s> BIB030
3.3.1 Early Summarisation Approaches. The earliest known work on automated summarisation dates back to 1958 when BIB001 proposed that the frequency of words could provide a measure of their importance in a document. In his work, he ranked words based on their frequencies and used the ranking of individual words in a sentence to calculate their significance, finally selecting the top ranked sentences as the summary. BIB002 used sentence position as a feature for selecting important sentences in a document (e.g., for news sentences, early sentences are more important than later sentences). BIB003 extended this work on summarisation and proposed a extensible model for extractive summarisation, which later on came to be known as the Edmundsonian Paradigm  . In this proposed model, the author used a weighted linear equation to combine four features to score text nuggets (e.g., sentences) for summary inclusion: where α, β, γ and δ are manually assigned weights; W is the overall weight of sentence s, C represents the score given to sentence s due to the presence of cue words (bonus words or stigma words) extracted from a corpus, K represents the score given for key words (based on word frequency), L is the score given based on sentence location features, and T is the weight assigned based on terms in the sentence that are also present in the title. Such a statistical approach to sentence selection has been very popular in the research community with Earl [1970] being the first to experiment with a variety of shallow lexical features. More recently, numerous statistical approaches using multiple words, noun phrases, main verbs, and named entities. have been proposed BIB008 BIB018 BIB025   ], while some research focused on utilising shallow lexical items with salient properties (e.g., subheadings) BIB009 BIB020 or via the analysis of topical content BIB019 . The importance of the discourse structure of a text was realised quite early in summarisation research, and this property has been heavily exploited ever since BIB004 BIB012  24 , which was initiated from the Text Summarisation track of the DUC and the Question Answering track of the TREC. Since the commencement of widespread research on ATS, a branch of research has focused on the application of machine learning algorithms for text summarisation. In most of the initial research, machine learning was employed sparingly, as preliminary steps  . Early research mostly assumed feature independence and used the Naïve Bayes classifier BIB006 BIB010  with various features including those used in the Edmundsonian Paradigm. Later research introduced the use of richer feature sets and a range of machine learning algorithms. For example, BIB015 [2007] . While one branch of research focused on machine learning approaches, another branch progressed research on natural language analysis techniques. BIB005 and BIB012   use the Rhetorical Structure Theory (RST) 25 by building RST source text trees using discourse structure information, and identifying the nuclei of the trees to build the summaries. and utilise the PALSUMM model which uses more abstract discourse trees. BIB008 show the use of lexical chains -sequences of related words that can span short or long distances -for single-document summarisation. For example, and BIB021 use a template suited to the medical domain; BIB007 and BIB013 fill up template slots from a database of news information as a first step to their algorithm for news summarisation; and BIB030 show the use of templates to automatically generate Wikipedia articles. 3.3.3 Multi-document Summarisation. The field of multi-document summarisation was pioneered by the SUMMONS BIB007 BIB013 ] summarisation system, which employed a template-based summarisation approach. Extractive summarisation systems have been shown to work well for multiple documents particularly in the news domain, an example being the MEAD 26 system. Multi-document summarisation approaches suffer from the problems of incoherence and redundancy, and numerous approaches have been proposed to address them. One popular approach to reduce redundancy is the use of clustering. In this technique, common themes or concepts across document sets are identified and grouped or clustered together. Once the clusters are created, the summary can be generated by applying various algorithms that depend primarily on the content and compression needs. For example, some use a single sentence to represent each cluster in the final summary BIB007  , while some generate a composite sentence from each cluster through the use of information fusion so as to combine the most salient concepts from multiple sentences within a cluster BIB008 BIB016 BIB026 . Another approach that has been successfully applied to limit the selection redundant information when performing summarisation is Maximal Marginal Relevance (MMR). This technique, which is specifically useful for query-focused summarisation, text segments that are deemed relevant to a query, usually via some text similarity measure, are rewarded. At the same time, redundant text segments are penalised using the same similarity measure. The two similarity measures are combined using a linear equation in an attempt to balance redundancy and relevance. MMR was initially utilised for document retrieval and is given by the following formula: where, as explained by BIB014 : "Q is a query; R = IR(C, Q, θ), i.e., the ranked list of documents retrieved by an IR system given a document collection C and a relevance threshold θ; S is the subset of documents in R already selected; R\S is the set difference, i.e., the set of unselected documents in R; Sim 1 is the similarity metric used in document retrieval and relevance ranking between documents and a query; and Sim 2 can be the same or a different metric." Graph-based approaches have also been applied to text summarisation BIB011 BIB017 BIB022 BIB027 , with BIB011 being the pioneers in this area. In their approach, the authors use nodes to represent words and edges between nodes represent relationships. The summaries generated can be topic driven, and there is no text in the summaries. Instead, the summary content is represented via as nodes and edges that represent contents and relations between them. When summarising a pair of documents, common nodes represent same words or synonyms, while difference nodes are those that are not common. Sentence selection from the graph is computed from the average activated weights of the covered words: for a sentence s, its score in terms of coverage of common nodes is given by the following formula: where c(s) = {w|w ∈ Common s}. The score for differences is calculated similarly. The sentences with higher common and difference scores are selected for the final summary. BIB022 presented the LexRank system for multi-document summarisation, which is a graph-based system using a connected, undirected graph to represent documents. A similar method, suitable for single-document summarisation only, was proposed by BIB023 . Other graph based approaches have been proposed, both in the medical domain BIB028 BIB024 and outside it BIB029 . Similar to graph-based techniques are centroid-based summarisation techniques, first proposed by . The summarisation is done in three stages. The first stage involves grouping together news articles on the same topics (topic detection), with the goal of clustering together news stories based on the events they discuss. These groups are called clusters and the centroid for each cluster is computed. The centroid of a cluster is a pseudo-document consisting of a set of highly relevant words. The relevance of these words is determined using Term Frequency-Inverse Document Frequency (tf×idf ) measures-those having tf×idf values above a predetermined threshold are included in the centroid. Tf and idf values are computed using the following equations: where n i,j is the occurrence frequency of the considered term t i in document d j , and k n kj is the sum of the number of occurrences of all the terms in d j ; |D| is the total number of documents the corpus contains, and |{d : t i ∈ d}| is the total count of documents where the term t i occurs. In the second stage, salient topics are identified via the use of centroids, and the overall score for each sentence is computed by combining three distinct values-centroid, positional, and overlap with the first sentence-minus a penalty for redundancy.