Number of found records: 80
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SAGGION, Horacio; LAPALME, Guy |
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Selective Analysis for Automatic Abstracting: Evaluating Indicativeness. |
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PDF |
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We have developed a new methodology for automatic abstracting of scientific and technical articles called Selective Analysis. This methodology allows the generation of indicative-informative abstracts integrating different types of information extracted from the source text. The indicative part of the abstract identifies the topics of the document while the informative one elaborates some topics according to the reader's interest. The first evaluation of our methodology demonstrates that Selective Analysis performs well in the task of signaling the topic of the document demonstrating the viability of such a technique. The sentences the system produces from instantiated templates are considered to be as acceptable as human produced sentences. (AU) |
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automatic abstracting; selective analysis; indicative-informative abstract |
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SCHIFFMAN, Barry; NENKOVA, Ani; MCKEOWN, Kathleen R. |
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Experiments in multidocument Summarization |
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HLT ’02, San Diego, Calif., USA |
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PDF |
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This paper describes a multidocument summarizer built upon research into the detection of new information. The summarizer uses several new strategies to select interesting and informative sentences, including an innovative measure of importance derived from the analysis of a large corpus. The system also computes concept frequencies rather than word frequencies as an additional measure of importance. It merges these strategies with a number of familiar summarization heuristics to rank sentences. The initial version of the summarizer performed successfully in the evaluation reported at the Document Understanding Conference last year, although the system addressed only the content of the summary and not the presentation. We also discuss here the procedures we are developing to improve the presentation and readability of the summaries. (AU) |
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Importance metrics; text generation; text analysis |
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Sjöbergh, Jonas |
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Older versions of the ROUGE eval summarization evaluation system were easier to fool |
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Information Processing & Management, Nov 2007, Vol. 43 Issue 6, p1500-1505 |
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On line (04/2008) (Only UGR) |
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We show some limitations of the ROUGE evaluation method for automatic summarization. We present a method for automatic summarization based on a Markov model of the source text. By a simple greedy word selection strategy, summaries with high ROUGE-scores are generated. These summaries would however not be considered good by human readers. The method can be adapted to trick different settings of the ROUGEeval package (DB) |
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Automatic summaries; evaluation |
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SPARCK JONES, Karen |
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A research agenda for automated summarizing. A Research Platform for Intelligent Summarizing II |
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On line (11/05/2005) |
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Seminar on research in automated summarizing |
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automated summarizing; report (own) |
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