Liu Ruifang


2021

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Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization
Zhang Ximing | Liu Ruifang
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“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.”
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