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

ACL ID C10-2060
Title A Comparative Study on Ranking and Selection Strategies for Multi-Document Summarization
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

This paper presents a comparative study on two key problems existing in extrac- tive summarization: the ranking problem and the selection problem. To this end, we presented a systematic study of comparing different learning-to-rank al- gorithms and comparing different selec- tion 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 learn- ing-to-rank algorithms outperform the baselines significantly; (2) there is no significant difference among the learn- ing-to-rank algorithms; and (3) the in- teger linear programming selection strategy generally outperformed Maxi- mum Marginal Relevance and Diversity Penalty strategies.