Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <s> Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows ?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem reduces to finding potential answers in large volumes of data and validating their accuracy. We apply a method for arbitrary passage retrieval to the first half of the problem and demonstrate that answer redundancy can be used to address the second half. The success of our approach depends on the idea that the volume of available Web data is large enough to supply the answer to most factual questions multiple times and in multiple contexts. A query is generated from a question and this query is used to select short passages that may contain the answer from a large collection of Web data. These passages are analyzed to identify candidate answers. The frequency of these candidates within the passages is used to ``vote'' for the most likely answer. The approach is experimentally tested on questions taken from the TREC-9 question-answering test collection. As an additional demonstration, the approach is extended to answer multiple choice trivia questions of the form typically asked in trivia quizzes and television game shows. <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <s> generation based on rhetorical structure extraction. In Proceedings of the International Conference on Computational Linguistics, Kyoto, Japan, pages 344–348. Otterbacher, Jahna, Dragomir R. Radev, and Airong Luo. 2002. Revisions that improve cohesion in multi-document summaries: A preliminary study. In ACL Workshop on Text Summarization, Philadelphia. Papineni, K., S. Roukos, T. Ward, and W-J. Zhu. 2001. 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Computational Linguistics, 28(4), 447–485. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <s> This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <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> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <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> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview <s> Display Omitted We present an approach for query-focused extractive summarisation of medical text.Our approach is data-centric and utilises a specialised annotated corpus.We introduce target-sentence-specific summarisation to improve summarisation.We incorporate query information and domain-specific knowledge in various ways.Our approach performs significantly better than existing benchmark approaches. BackgroundEvidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. In this paper, we focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. MethodsWe utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents. We also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. ResultsIn automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line answers to practitioners' queries. ConclusionsOur research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data, we suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine. <s> BIB006
According to BIB002 , the intent of a summary is to express the informative contents of a document in a compressed manner.  provides a more formal definition and explains that the process of summarisation involves "taking an information source, extracting content from it, and presenting the most important content to the user in a condensed form and in a manner sensitive to the user's or application's needs." Sparck Jones [1999] explains that summarisation is a hard task because it requires the characterisation of a source text as a whole, "capturing its important content, where content is a matter of both information and its expression, and importance is a matter of what is essential as well as what is salient." The motivation for building automated summarisation systems has increased over time due to the increasing availability of web-based textual information, and the explosion of available information has necessitated intensive research in this area  . The number of online text documents has been increasing exponentially over the recent years, and this is also true for the medical domain BIB006 . Consequently, significant progress in automated summarisation has been made in the last two decades  . The prime advantage of having a large amount of available information is the redundancy in it. Research has shown how summarisation systems can exploit this redundancy BIB004 BIB001 BIB003 , particularly when summarising from multiple documents. As will be discussed later, many summarisation systems rely on redundancy (e.g., frequently occurring words, concepts, etc.) to generate automated summaries. In EBM, redundancy is beneficial as it gives stronger evidence on a given finding. As for the cons, the abundance of information also introduces the need for efficient information retrieval and extraction, both of which are difficult tasks. In many cases, identification of relevant information requires elaborate manual searching through redundant informa- tion, which is often quite time consuming and therefore rather inefficient BIB004 . "It has been realised that added value is not gained merely through larger quantities of data, but through easier access to the required information at the right time and in the most suitable form" BIB005 . This gives rise to the need for technologies that can gather required information for the users and present them in a simplified, concise and friendly manner. Thus, we can say that while the large amount of information is a necessary condition for the development of automated summarisation systems, it also introduces the problem of information overload.