書誌事項
- タイトル別名
-
- A Method for Extracting Important Segments from Documents Using Support Vector Machines: Toward Automatic Text Summarization
- 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
この論文をさがす
説明
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.
収録刊行物
-
- 人工知能学会論文誌
-
人工知能学会論文誌 21 330-339, 2006
一般社団法人 人工知能学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390282680084262528
-
- NII論文ID
- 10022006487
-
- NII書誌ID
- AA11579226
-
- ISSN
- 13468030
- 13460714
-
- NDL書誌ID
- 8686502
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- NDLサーチ
- Crossref
- CiNii Articles
- OpenAIRE
-
- 抄録ライセンスフラグ
- 使用不可
