# A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

arXiv: https://arxiv.org/abs/1805.03616 · HTML: https://mlsanity.com/paper/1805.03616/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 21 of 21)

- [Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning](https://mlsanity.com/paper/2108.05067/) (2021)
- [Enriching and Controlling Global Semantics for Text Summarization](https://mlsanity.com/paper/2109.10616/) (2021)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
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- [Keep It Private: Unsupervised Privatization of Online Text](https://mlsanity.com/paper/2405.10260/) (2024)
- [Sketch and Customize: A Counterfactual Story Generator](https://mlsanity.com/paper/2104.00929/) (2021)
- [Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition](https://mlsanity.com/paper/2101.03287/) (2021)
- [Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning](https://mlsanity.com/paper/2406.00303/) (2024)
- [IOT: Instance-wise Layer Reordering for Transformer Structures](https://mlsanity.com/paper/2103.03457/) (2021)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production](https://mlsanity.com/paper/2302.09300/) (2023)
- [Neural Abstractive Unsupervised Summarization of Online News Discussions](https://mlsanity.com/paper/2106.03953/) (2021)
- [Controllable Topic-Focused Abstractive Summarization](https://mlsanity.com/paper/2311.06724/) (2023)
- [Topic-Aware Encoding for Extractive Summarization](https://mlsanity.com/paper/2112.09572/) (2021)
- [SASS: Data and Methods for Subject Aware Sentence Simplification](https://mlsanity.com/paper/2303.14589/) (2023)
- [Boosting Summarization with Normalizing Flows and Aggressive Training](https://mlsanity.com/paper/2311.00588/) (2023)
- [Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization](https://mlsanity.com/paper/2509.09852/) (2025)
- [LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation](https://mlsanity.com/paper/2604.25665/) (2026)

## Abstract (arXiv)

In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non-differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.