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Abstract

Experimental subjects wrote abstracts using a simplified version of the TEXNET abstracting assistance software. In addition to the full text, subjects were presented with either keywords or phrases extracted automatically. The resulting abstracts, and the times taken, were recorded automatically; some additional information was gathered by oral questionnaire. Selected abstracts produced were evaluated on various criteria by independent raters. Results showed considerable variation among subjects, but 37% found the keywords or phrases 'quite' or 'very' useful in writing their abstracts. Statistical analysis failed to support several hypothesized relations: phrases were not viewed as significantly more helpful than keywords; and abstracting experience did not correlate with originality of wording, approximation of the author abstract, or greater conciseness. Requiring further study are some unanticipated strong correlations including the following: Windows experience and writing an abstract like the author's; experience reading abstracts and thinking one had written a good abstract; gender and abstract length; gender and use of words and phrases from the original text. Results have also suggested possible modifications to the TEXNET software

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Abstract

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.

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Abstract

In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.

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Date

22. 7.2006 17:25:48