Extractive summarization using supervised and semi-supervised learning

  • Authors:

  • Kam-Fai Wong;Mingli Wu;Wenjie Li

  • Affiliations:

  • The Chinese University of Hong Kong, New Territories, Hong Kong;The Chinese University of Hong Kong, New Territories, Hong Kong and The Hong Kong Polytechnic University, Kowloon, Hong Kong;The Hong Kong Polytechnic University, Kowloon, Hong Kong

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. 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 represent sentences by events they contained. Relevance features evaluate a sentence from its relatedness with other sentences. Experiments show that the combined features improved summarization performance significantly. Although the evaluation results are encouraging, supervised learning approach requires much labeled data. Therefore we investigate co-training by combining labeled and unlabeled data. Experiments show that this semi-supervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost.