A Comparative Study on Ranking and Selection Strategies for Multi-Document Summarization

This paper presents a comparative study on two key problems existing in extractive summarization: the ranking problem and the selection problem. To this end, we presented a systematic study of comparing different learning-to-rank algorithms and comparing different selection strategies. This is the first work of providing systematic analysis on these problems. Experimental results on two benchmark datasets demonstrate three findings: (1) pairwise and listwise learning-to-rank algorithms outperform the baselines significantly; (2) there is no significant difference among the learning-to-rank algorithms; and (3) the integer linear programming selection strategy generally outperformed Maximum Marginal Relevance and Diversity Penalty strategies.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Feng Jin, Minlie Huang, Xiaoyan Zhu
• Keywords: COLING 2010, Computational Linguistics, Diversity Penalty Strategies, Learning-to-rank Algorithms, Linear Programming Selection