Knowledge-Based Systems Volume 221, 7 June 2021, 106973
Abstract:
Abstractive 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.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Natural Science Foundation of China - not mentioned
Grant Reference no. : No. 61936012
This research / project is supported by the National Natural Science Foundation of China - not mentioned
Grant Reference no. : No. 61772324
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. :