Summarization Based on Task-Oriented Discourse Parsing
Xun Wang, Yasuhisa Yoshida, Tsutomu Hirao, Xun Wang, Yasuhisa Yoshida, Tsutomu Hirao, Katsuhito Sudoh, Masaaki Nagata, Katsuhito Sudoh, Masaaki Nagata · IEEE/ACM Transactions on Audio Speech and Language Processing · 2015
Previous research demonstrates that discourse relations can help generate high-quality summaries. Existing studies usually adopt existing discourse parsers directly with no modifications, hence cannot take full advantage of discourse parsing. This paper describes a new single document summarization system. In contrast to previous work, we train a discourse parser specially for summarization by using summaries. The training data are dynamically changed during the training phase to enable the parser to grab the text units that are important for summaries. A special tree-based summary extraction algorithm is designed to work with the new parser. The proposed system enables us to combine discourse parsing and summarization in a unified scheme. Experiments on both the RST-DT and DUC2001 datasets show the effectiveness of the proposed method.