Title: Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking  for Extractive Summarization

Abstract: Extractive text summarization seeks to extract indicative sentences from a source document and assemble them to form a summary. Selecting salient but not redundant sentences has always been the main challenge. Unlike the previous two-stage strategies, this paper presents a unified end-to-end model, learning to rerank the sentences by modeling salience and redundancysimultaneously. 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 information. 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 at obtaining a summary with a high summary-level score. The experimental results and evaluation show that our method outperforms the strong baselines on three datasets and further boosts the quality of candidate summaries, which intensely indicate the effectiveness of the proposed framework.

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