# Topic-Guided Abstractive Text Summarization: a Joint Learning Approach

arXiv: https://arxiv.org/abs/2010.10323 · HTML: https://mlsanity.com/paper/2010.10323/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions](https://mlsanity.com/paper/2410.20019/) (2024)
- [Boosting Summarization with Normalizing Flows and Aggressive Training](https://mlsanity.com/paper/2311.00588/) (2023)
- [InforME: Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience](https://mlsanity.com/paper/2510.05769/) (2025)

## Abstract (arXiv)

We introduce a new approach for abstractive text summarization, Topic-Guided Abstractive Summarization, which calibrates long-range dependencies from topic-level features with globally salient content. The idea is to incorporate neural topic modeling with a Transformer-based sequence-to-sequence (seq2seq) model in a joint learning framework. This design can learn and preserve the global semantics of the document, which can provide additional contextual guidance for capturing important ideas of the document, thereby enhancing the generation of summary. We conduct extensive experiments on two datasets and the results show that our proposed model outperforms many extractive and abstractive systems in terms of both ROUGE measurements and human evaluation. Our code is available at: https://github.com/chz816/tas.