Document Summarization Using Conditional Random Fields

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 ass...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Dou Shen, Jian-Tao Sun, Hua Li, Qiang Yang, Zheng Chen
• Keywords: Artificial Intelligence, Extractive Document Summarization, IJCAI 2007, Most Supervised Methods, Summarization Task