Paper: Extractive Summarization Using Supervised and Semi-Supervised Learning

ACL ID C08-1124
Title Extractive Summarization Using Supervised and Semi-Supervised Learning
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors
  • Kam-Fai Wong (The Chinese University of Hong Kong, Shatin Hong Kong)
  • Mingli Wu (The Chinese University of Hong Kong, Shatin Hong Kong; Hong Kong Polytechnic University, Hung Hom Hong Kong)
  • Wenjie Li (Hong Kong Polytechnic University, Hung Hom Hong Kong)

It is difficult to identify sentence impor- tance from a single point of view. In this paper, we propose a learning-based ap- proach to combine various sentence fea- tures. They are categorized as surface, content, relevance and event features. Surface features are related to extrinsic aspects of a sentence. Content features measure a sentence based on content- conveying words. Event features repre- sent sentences by events they contained. Relevance features evaluate a sentence from its relatedness with other sentences. Experiments show that the combined fea- tures improved summarization perform- ance significantly. Although the evalua- tion results are encouraging, supervised learning approach requires much labeled data. Therefore we investigate co-training by combining labele...