A Method for Extracting Important Segments from Documents Using Support Vector Machines: Toward Automatic Text Summarization

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  • Suzuki Daisuke
    Department of Systems Engineering, The University of Electro-Communications
  • Utsumi Akira
    Department of Systems Engineering, The University of Electro-Communications

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Other Title
  • Ｓｕｐｐｏｒｔ　Ｖｅｃｔｏｒ　Ｍａｃｈｉｎｅを用いた文書の重要文節抽出―要約文生成に向けて―
  • Support Vector Machine オ モチイタ ブンショ ノ ジュウヨウ ブンセツ チュウシュツ ヨウヤクブン セイセイ ニ ムケテ
  • Toward Automatic Text Summarization
  • 要約文生成に向けて

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In this paper we propose an extraction-based method for automatic summarization. The proposed method consists of two processes: important segment extraction and sentence compaction. The process of important segment extraction classifies each segment in a document as important or not by Support Vector Machines (SVMs). The process of sentence compaction then determines grammatically appropriate portions of a sentence for a summary according to its dependency structure and the classification result by SVMs. To test the performance of our method, we conducted an evaluation experiment using the Text Summarization Challenge (TSC-1) corpus of human-prepared summaries. The result was that our method achieved better performance than a segment-extraction-only method and the Lead method, especially for sentences only a part of which was included in human summaries. Further analysis of the experimental results suggests that a hybrid method that integrates sentence extraction with segment extraction may generate better summaries.

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