Frame Semantics guided network for Abstractive Sentence Summarization

Text Summarization is an important and practical task, aiming to rephrase the input text into a short version summary, while preserving its same and important semantics. In this paper, we propose a novel Frame Semantics guided network for Abstractive Sentence Summarization (FSum), which is able to learn a better text semantic representation by selecting more relevant Frame semantics from text, and integrating Frame semantic representation with text representation effectively. Extensive experiments demonstrate that our proposed FSum model performs significantly better than existing state-of-the-art techniques on both Gigaword and DUC 2004 benchmark datasets. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a new Frame Semantics guided network for Abstractive Sentence Summarization, which can learn better text semantic representation and significantly outperforms existing techniques in extensive experiments.