# Abstractive Text Summarization by Incorporating Reader Comments

arXiv: https://arxiv.org/abs/1812.05407 · HTML: https://mlsanity.com/paper/1812.05407/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://mlsanity.com/paper/2305.13091/) (2023)
- [Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization](https://mlsanity.com/paper/2112.12072/) (2021)
- [SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads](https://mlsanity.com/paper/2312.16733/) (2023)
- [Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications](https://mlsanity.com/paper/2409.18454/) (2024)
- [A Topic-aware Summarization Framework with Different Modal Side Information](https://mlsanity.com/paper/2305.11503/) (2023)
- [CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level](https://mlsanity.com/paper/2110.10874/) (2021)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [UMSE: Unified Multi-scenario Summarization Evaluation](https://mlsanity.com/paper/2305.16895/) (2023)
- [A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization](https://mlsanity.com/paper/2305.08503/) (2023)
- [Stick to Facts: Towards Fidelity-oriented Product Description Generation](https://mlsanity.com/paper/2503.08454/) (2025)
- [COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation](https://mlsanity.com/paper/2506.15372/) (2025)
- [Neural Abstractive Unsupervised Summarization of Online News Discussions](https://mlsanity.com/paper/2106.03953/) (2021)
- [Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge](https://mlsanity.com/paper/2305.09877/) (2023)
- [Advancements in Natural Language Processing for Automatic Text Summarization](https://mlsanity.com/paper/2502.19773/) (2025)
- [Improving Multilingual Social Media Insights: Aspect-based Comment Analysis](https://mlsanity.com/paper/2505.23037/) (2025)

## Abstract (arXiv)

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research.