Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text 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> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text Summarisation <s> We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text Summarisation <s> In this paper we describe our method for the summarization of legal documents helping a legal expert determine the key ideas of a judgment. Our approach is based on the exploration of the document’s architecture and its thematic structures in order to build a table style summary for improving coherency and readability of the text. We present the components of a system, called LetSum, built with this approach, its implementation and some preliminary evaluation results. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text Summarisation <s> Objective:: The aim of this paper is to survey the recent work in medical documents summarization. Background:: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology:: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions:: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text 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> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text Summarisation <s> The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline. <s> BIB006 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Factors of Text Summarisation <s> Objective: Access to the vast body of research literature that is available in biomedicine and related fields may be improved by automatic summarisation. This paper presents a method for summarising biomedical scientific literature that takes into consideration the characteristics of the domain and the type of documents. Methods: To address the problem of identifying salient sentences in biomedical texts, concepts and relations derived from the Unified Medical Language System (UMLS) are arranged to construct a semantic graph that represents the document. A degree-based clustering algorithm is then used to identify different themes or topics within the text. Different heuristics for sentence selection, intended to generate different types of summaries, are tested. A real document case is drawn up to illustrate how the method works. Results: A large-scale evaluation is performed using the recall-oriented understudy for gisting-evaluation (ROUGE) metrics. The results are compared with those achieved by three well-known summarisers (two research prototypes and a commercial application) and two baselines. Our method significantly outperforms all summarisers and baselines. The best of our heuristics achieves an improvement in performance of almost 7.7 percentage units in the ROUGE-1 score over the LexRank summariser (0.7862 versus 0.7302). A qualitative analysis of the summaries also shows that our method succeeds in identifying sentences that cover the main topic of the document and also considers other secondary or ''satellite'' information that might be relevant to the user. Conclusion: The method proposed is proved to be an efficient approach to biomedical literature summarisation, which confirms that the use of concepts rather than terms can be very useful in automatic summarisation, especially when dealing with highly specialised domains. <s> BIB007
Automated text summarisers must take into account a number of factors to achieve their goals. Here we discuss some of these factors, knowledge of which is essential to understand the process of text summarisation. The factors affecting ATS can be grouped into three main categories: input, purpose and output. We primarily draw the following information from existing literature   BIB004  , and provide references to articles containing more detail in specific cases. 3.2.1 Input. The following factors are associated with the inputs of a summarisation system: Unit -Summarisers can take as input either one document or multiple documents. Summarising multiple documents represents a more difficult task than summarising single documents, and, additional algorithms are required to overcome problems of redundancy (different documents might present identical information which could lead to repetitions in the summary), inconsistency (the information presented in distinct documents may not be consistent), and incoherence (information extracted from separate documents may not be coherent when presented together). Language -A summarising system can be mono-lingual, multi-lingual or cross-lingual. Domain -Summarisation systems can either be domain-specific or domain-independent. Domainindependent approaches are applicable to documents from various domains without requiring any changes in the algorithm. While having the benefit of portability to different domains, domainindependent approaches fail to take advantage of knowledge and resources available to specific domains. Domain-specific systems are designed for specific domains and use all the available resources and knowledge available for the relevant domain. There has been significant research in text summarisation specific to domains such as news BIB005 , scientific BIB007 , legal BIB003 , and medical BIB006  . Structure -This refers to the external structure of documents, such as headings, boxes, tables, and also more subtle exploitable structural information such as rhetorical patterns. The structure of documents can vary between domains. For example, news stories do not generally have much explicit structure other than top-level headlines  , while technical articles contain more structure which can be exploited by summarisation systems BIB001 BIB002 BIB006 . Medical article abstracts may be structured or unstructured, and this factor plays a vital role in the performance of summarisers. Meta-data -In some cases, header information or meta-data associated with input documents play an important role in summarisation. For example, dates of publications are crucial for news items. In the case of the medical domain, databases such as MEDLINE use meta-data to indicate the key topics in each article. 3.2.2 Purpose. These factors are associated with the purpose of the summaries. In other words, these factors determine what a summary is for or what a summary is like. Summary Type -Summaries can either be extractive or abstractive. Extractive summarisation involves extracting content from the source document(s) and presenting them as the summary. Abstractive summarisation, in contrast, involves discovering the most salient contents in the source document(s), aggregating them, and presenting them in a concise manner (e.g. via techniques that automatically generate natural language) BIB004 . Information -Summaries can be generic or user-oriented. Generic summaries only take into account the information found in the input document(s), whereas user-oriented summaries attempt to extract and summarise only the information that are needed to fulfill information requirements of the users. The summarisation query enables the user to formulate the information needs. Use -This is the most important purpose factor  , and has a major influence on summary content and presentation. Audience -Summaries must take into account the intended audience. Different summaries can be generated from the same input sources to suit the requirements of the audience. News material, for example, "has at least two audiences: ordinary readers and analysts"  . Time -Time is a critical factor for some summarisation systems. For example, query-oriented summaries need to be delivered promptly, and thus, corresponding systems need to be very fast. 3.2.3 Output. These factors are associated with the summarised output. Coverage -Coverage of a set of sources by a summary can be either comprehensive or selective  . Reflective summaries are comprehensive (i.e., summarises the whole source text), while query-or topic-oriented summaries are selective (i.e., summarises only a segment of the source text that contains information associated with the query/topic)  . Compression -This refers to the amount by which the information from the source(s) are reduced during summarisation. Structure -The output of a summary may be plain text or the information may be represented using tables (e.g.,  ), forms BIB003 ), and lists (e.g., ) to name a few.