# TWAG: A Topic-Guided Wikipedia Abstract Generator

arXiv: https://arxiv.org/abs/2106.15135 · HTML: https://mlsanity.com/paper/2106.15135/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 29 Jun 2021.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2005.10043/) (2020)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://mlsanity.com/paper/2011.07832/) (2020)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia](https://mlsanity.com/paper/1608.03542/) (2016)
- [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://mlsanity.com/paper/1906.04687/) (2019)
- [Graph-based Neural Sentence Ordering](https://mlsanity.com/paper/1912.07225/) (2019)
- [Multi-document abstractive summarization using ILP based multi-sentence compression](https://mlsanity.com/paper/1609.07034/) (2016)
- [Abstractive Multi-Document Summarization via Phrase Selection and Merging](https://mlsanity.com/paper/1506.01597/) (2015)
- [Towards a Neural Network Approach to Abstractive Multi-Document Summarization](https://mlsanity.com/paper/1804.09010/) (2018)

## Built on by (most cited first, 5 of 5)

- [Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness](https://mlsanity.com/paper/2205.01889/) (2022)
- [WikiVideo: Article Generation from Multiple Videos](https://mlsanity.com/paper/2504.00939/) (2025)
- [MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization](https://mlsanity.com/paper/2406.03479/) (2024)
- [Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods](https://mlsanity.com/paper/2206.04317/) (2022)
- [Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition](https://mlsanity.com/paper/2405.14470/) (2024)

## Abstract (arXiv)

Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that modelnames outperforms various existing baselines and is capable of generating comprehensive abstracts. Our code and dataset can be accessed at urlhttps://github.com/THU-KEG/TWAG