@inproceedings{6a3667a44d5e4e6a99b010c8308630cd,
title = "Subtopic-based multi-document summarization",
abstract = "This paper proposes a novel approach for multi-document summarization based on subtopic segmentation. It firstly detects the subtopics in a topic, and then finds the central sentence for each subtopic. The sentences are scored based on their importance in the document and in the subtopic. Two anti-redundancy strategies are used to extract sentences to form summarization. Since our approach is intrinsically incremental, it is effective when new documents are added to the document set. Experimental results indicate that the proposed approach is effective and efficient.",
keywords = "Multi-document summarization; Topic segmentation; Topic Detection and Tracking; Anti-redundancy strategy",
author = "Lin Dai and Tang, {Ji Liang} and Xia, {Yun Qing}",
year = "2009",
doi = "10.1109/ICMLC.2009.5212767",
language = "English",
isbn = "9781424437030",
series = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",
pages = "3505--3510",
booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",
note = "2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",
}