A Novel Relational Learning-to-Rank Approach for Topic-Focused Multi-document Summarization

@article{Zhu2013ANR,
  title={A Novel Relational Learning-to-Rank Approach for Topic-Focused Multi-document Summarization},
  author={Yadong Zhu and Yanyan Lan and Jiafeng Guo and Pan Du and Xueqi Cheng},
  journal={2013 IEEE 13th International Conference on Data Mining},
  year={2013},
  pages={927-936}
}
Topic-focused multi-document summarization aims to produce a summary over a set of documents and conveys the most important aspects of a given topic. Most existing extractive methods view the task as a multi-criteria ranking problem over sentences, where relevance, salience and diversity are three typical requirements. However, diversity is a challenging problem as it involves modeling the relationship between sentences during ranking, where traditional methods usually tackle it in a heuristic… CONTINUE READING

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