Reinforced Abstractive Summarization with Adaptive Length Controlling

• URL: http://arxiv.org/abs/2112.07534v1
• Date: Tue, 14 Dec 2021 16:48:47 GMT
• Title: Reinforced Abstractive Summarization with Adaptive Length Controlling
• Authors: Mingyang Song, Yi Feng, Liping Jing
• Abstract summary: Controllable summarization, especially of the length, is an important issue for some practical applications. We propose an textbfAdaptive textbfLength textbfControlling textbfOptimization (textbfALCO) method to leverage two-stage abstractive summarization model.
• Score: 12.793451906532223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document summarization, as a fundamental task in natural language generation, aims to generate a short and coherent summary for a given document. Controllable summarization, especially of the length, is an important issue for some practical applications, especially how to trade-off the length constraint and information integrity. In this paper, we propose an \textbf{A}daptive \textbf{L}ength \textbf{C}ontrolling \textbf{O}ptimization (\textbf{ALCO}) method to leverage two-stage abstractive summarization model via reinforcement learning. ALCO incorporates length constraint into the stage of sentence extraction to penalize the overlength extracted sentences. Meanwhile, a saliency estimation mechanism is designed to preserve the salient information in the generated sentences. A series of experiments have been conducted on a wildly-used benchmark dataset \textit{CNN/Daily Mail}. The results have shown that ALCO performs better than the popular baselines in terms of length controllability and content preservation.

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