@inproceedings{ximing-ruifang-2021-jointly,
    title = "Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization",
    author = "Ximing, Zhang  and
      Ruifang, Liu",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ccl-1.85/",
    pages = "952--963",
    language = "eng",
    abstract = "Extractive text summarization seeks to extract indicative sentences from a source document andassemble them to form a summary. Selecting salient but not redundant sentences has alwaysbeen the main challenge. Unlike the previous two-stage strategies this paper presents a unifiedend-to-end model learning to rerank the sentences by modeling salience and redundancy simul-taneously. Through this ranking mechanism our method can improve the quality of the overall candidate summary by giving higher scores to sentences that can bring more novel informa-tion. We first design a summary-level measure to evaluate the cumulating gain of each candidate summaries. Then we propose an adaptive training objective to rerank the sentences aiming atobtaining a summary with a high summary-level score. The experimental results and evalua-tion show that our method outperforms the strong baselines on three datasets and further booststhe quality of candidate summaries which intensely indicate the effectiveness of the proposed framework."
}