Regina Barzilay, Kathleen McKeown
"Sentence Fusion for Multidocument News Summarization",
Computational Linguistics, 2005.
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.
1. Introduction
Redundancy in large text collections, for natural language systems:
problems : difficulties for end users of search engines and news providers
opportunitie: can be exploited to identify important and accurate information for applications such as summarization and question answering
It would be highly desirable to have a mechanism that could identify common information among multiple related documents and fuse it into a coherent text. This article presents a method for sentence fusion that exploits redundancy to achieve this task in the context of multidocument summarization.
2. Framework for Sentence Fusion: MultiGen
3. Sentence Fusion
4. Sentence Fusion Evaluation
5. Related Work
6. Conclusions and Future Work
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