Enriching and Controlling Global Semantics for Text Summarization

TL;DR

This paper enhances Transformer-based abstractive summarization by integrating a neural topic model with normalizing flow to capture global semantics and introduces a control mechanism to balance this information, improving performance across multiple datasets.

Contribution

It introduces a neural topic model with normalizing flow for global semantics and a control mechanism to improve summarization quality.

Findings

Outperforms state-of-the-art models on five datasets.

Effectively captures global document semantics.

Balances global semantics with contextual information.

Abstract

Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.