Number of found records: 80
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Reeve, Lawrence H.; Han, Hyoil; Brooks, Ari D |
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The use of domain-specific concepts in biomedical text summarization |
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Information Processing & Management, Nov2007, Vol. 43 Issue 6, p1765-1776 |
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On line (04/2008) (Only UGR) |
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ext 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. (DB) |
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Automatic abstracts; biomedicine |
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SAGGION, Horacio |
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Using Linguistic Knowledge in Automatic Abstracting |
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37th Annual Meeting of the Association for Computational Linguistics 1999 |
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PDF |
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We present work on the automatic generation of short indicative-informative abstracts of scientific and technical articles. The indicative part of the abstract identifies the topics of the document while the informative part of the abstract elaborate some topics according to the reader's interest by motivating the topics, describing entities and defining concepts. We have defined our method of automatic abstracting by studying a corpus professional abstracts. The method also considers the reader's interest as essential in the process of abstracting. (AU) |
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automatic generation abstracts; scientific and technical article |
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SAGGION, Horacio; LAPALME, Guy |
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Concept identification and presentation in the context of technical text summarization |
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Automatic Summarization Workshop at NAACL/ANLP'2000. |
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PDF |
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We describe a method of text summarization that produces indicative-informative abstracts for technical papers. The abstracts are generated by a process of conceptual identification, topic extraction and re-generation. We have carried out an evaluation to assess indicativeness and text acceptability relying on human judgement. The results so far indicate good performance in both tasks when compared with other summarization technologies. (AU) |
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text summarization; indicative-informative abstracts; topic extraction; evaluation |
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SAGGION, Horacio; LAPALME, Guy |
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Where does information come from? Corpus Analysis Automatic Abstracting |
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RIFRA'98 (Sfax,Tunesie). |
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PDF |
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We report on our study of a corpus of abstracts and parent documents to determinate which structural parts of the parent document are used to extract useful information for an abstract. The results give us a sound basis for automatic abstracting of research articles. Our method for automatic abstracting, called selective analysis, is intended to produce user-oriented abstracts which are indicative in the essential content of the document and informative in the user's interest. (AU) |
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automatic abstracting; structural part; extract; selective analysis |
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