A General Contextualized Rewriting Framework for Text Summarization
Guangsheng Bao, Yue Zhang

TL;DR
This paper introduces a contextualized rewriting framework for text summarization that leverages entire document context and summary discourse, significantly improving ROUGE scores over previous non-contextualized methods.
Contribution
It formalizes a new approach using seq2seq with group-tag alignments for contextualized rewriting, enhancing extractive summaries with better context understanding.
Findings
Significant ROUGE score improvements over non-contextualized systems
Effective modeling of document and summary context without reinforcement learning
Outperforms existing rewriting systems across multiple extractors
Abstract
The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive model. Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context. In this paper, we investigate contextualized rewriting, which consumes the entire document and considers the summary context. We formalize contextualized rewriting as a seq2seq with group-tag alignments, introducing group-tag as a solution to model the alignments, identifying extractive sentences through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning, achieving strong improvements on ROUGE scores upon multiple extractors.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Text Readability and Simplification
MethodsTanh Activation · Sigmoid Activation · Long Short-Term Memory · Sequence to Sequence
