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.
REF ) made a comparative study on sentence/concept selection and pairwise and list ranking algorithms, and concluded ILP performed better than MMR and the diversity penalty strategy in sentence/concept selection.
A Comparative Study on Ranking and Selection Strategies for Multi-Document Summarization
COLING - POSTERS
Computer Science