# Topic Augmented Generator for Abstractive Summarization

arXiv: https://arxiv.org/abs/1908.07026 · HTML: https://mlsanity.com/paper/1908.07026/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Focus Attention: Promoting Faithfulness and Diversity in Summarization](https://mlsanity.com/paper/2105.11921/) (2021)
- [Enriching and Controlling Global Semantics for Text Summarization](https://mlsanity.com/paper/2109.10616/) (2021)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)
- [Topic Modeling Based Extractive Text Summarization](https://mlsanity.com/paper/2106.15313/) (2021)
- [Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods](https://mlsanity.com/paper/2206.04317/) (2022)
- [InforME: Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience](https://mlsanity.com/paper/2510.05769/) (2025)

## Abstract (arXiv)

Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document. The latent topics, identified by a topic model such as LDA, reveals more global semantic information that can be used to bias the decoder to generate words. In particular, they enable the decoder to have access to additional word co-occurrence statistics captured at document corpus level. We empirically validate the advantage of the proposed approach on both the CNN/Daily Mail and the WikiHow datasets. Concretely, we attain strongly improved ROUGE scores when compared to state-of-the-art models.