2007 DocumentSummUsingCRFs

(Shen et al., 2007) ⇒ Dou Shen, Jian-Tao Sun, Hua Li, Qiang Yang, and Zheng Chen. (2007). “Document Summarization Using Conditional Random Fields.” In: Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI 2007)

Subject Headings: Multi-Document Summarization Algorithm.

Notes

Cited By

~56 http://scholar.google.com/scholar?q=%22Document+summarization+using+conditional+random+fields%22+2007

Quotes

Abstract

Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a twoclass classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 DocumentSummUsingCRFs				Document Summarization Using Conditional Random Fields			http://Document Summarization Using Conditional Random Fields