Inferring Strategies for Sentence Ordering in Multidocument News Summarization
Regina Barzilay,Noémie Elhadad +1 more
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TLDR
A strategy for ordering information that combines constraints from chronological order of events and topical relatedness is implemented and Evaluation of the augmented algorithm shows a significant improvement of the ordering over two baseline strategies.Abstract:
The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.read more
Citations
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Journal ArticleDOI
Sentence Fusion for Multidocument News Summarization
TL;DR: This article introduces sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents that 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.
Journal ArticleDOI
Automatic Evaluation of Information Ordering: Kendall's Tau
TL;DR: An evaluation method based on Kendall's, a metric of rank correlation, is proposed that is inexpensive, robust, and representation independent and it is shown that Kendall's correlates reliably with human ratings and reading times.
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Applications of text mining within systematic reviews
TL;DR: It is concluded that text mining technologies do have the potential to assist at various stages of the review process, however, they are relatively unknown in the systematic reviewing community, and substantial evaluation and methods development are required before their possible impact can be fully assessed.
Book ChapterDOI
Automatic Text Summarization: Past, Present and Future
Horacio Saggion,Thierry Poibeau +1 more
TL;DR: This paper gives a short overview of summarization methods and evaluation and the number of interesting summarization topics being proposed in different contexts by end users.
Journal ArticleDOI
Recent advances in document summarization
TL;DR: Significant contributions made in recent years are emphasized, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences.
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