# Topic-Selective Graph Network for Topic-Focused Summarization

arXiv: https://arxiv.org/abs/2302.13106 · HTML: https://mlsanity.com/paper/2302.13106/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [A Contrastive Framework for Neural Text Generation](https://mlsanity.com/paper/2202.06417/) (2022)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations](https://mlsanity.com/paper/1910.01335/) (2019)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [NEWTS: A Corpus for News Topic-Focused Summarization](https://mlsanity.com/paper/2205.15661/) (2022)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Multiplex Graph Neural Network for Extractive Text Summarization](https://mlsanity.com/paper/2108.12870/) (2021)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)

## Built on by (most cited first, 4 of 4)

- [MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming](https://mlsanity.com/paper/2505.17147/) (2025)
- [Safety Alignment via Constrained Knowledge Unlearning](https://mlsanity.com/paper/2505.18588/) (2025)
- [Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling](https://mlsanity.com/paper/2505.17110/) (2025)
- [HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks](https://mlsanity.com/paper/2503.16342/) (2025)

## Abstract (arXiv)

Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.