Reinforced Abstractive Text Summarization With Semantic Added Reward

Reinforced Abstractive Text Summarization With Semantic Added Reward

Text summarization is an important task in natural language processing (NLP). Neural summary models summarize information by understanding and rewriting documents through the encoder-decoder structure. Recent studies have sought to overcome the bias that cross-entropy-based learning methods can have...

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Bibliographic Details
Main Authors: Heewon Jang, Wooju Kim
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Text summarization
abstract summarization
reinforcement learning
semantic similarity
Online Access:https://ieeexplore.ieee.org/document/9483920/

Internet

https://ieeexplore.ieee.org/document/9483920/

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