# Generating Summaries with Topic Templates and Structured Convolutional Decoders

arXiv: https://arxiv.org/abs/1906.04687 · HTML: https://mlsanity.com/paper/1906.04687/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 20 of 20)

- [Exploring Neural Models for Query-Focused Summarization](https://mlsanity.com/paper/2112.07637/) (2021)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Models and Datasets for Cross-Lingual Summarisation](https://mlsanity.com/paper/2202.09583/) (2022)
- [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://mlsanity.com/paper/2206.11249/) (2022)
- [WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus](https://mlsanity.com/paper/2304.04358/) (2023)
- [Adaptive Memory Replay for Continual Learning](https://mlsanity.com/paper/2404.12526/) (2024)
- [Self-Supervised Multimodal Opinion Summarization](https://mlsanity.com/paper/2105.13135/) (2021)
- [A Sliding-Window Approach to Automatic Creation of Meeting Minutes](https://mlsanity.com/paper/2104.12324/) (2021)
- [Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness](https://mlsanity.com/paper/2205.01889/) (2022)
- [Learning to Plan for Language Modeling from Unlabeled Data](https://mlsanity.com/paper/2404.00614/) (2024)
- [Text Summarization with Latent Queries](https://mlsanity.com/paper/2106.00104/) (2021)
- [TWAG: A Topic-Guided Wikipedia Abstract Generator](https://mlsanity.com/paper/2106.15135/) (2021)
- [Surfer100: Generating Surveys From Web Resources, Wikipedia-style](https://mlsanity.com/paper/2112.06377/) (2021)
- [MegaWika 2: A More Comprehensive Multilingual Collection of Articles and their Sources](https://mlsanity.com/paper/2508.03828/) (2025)
- [Modeling Endorsement for Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2110.07844/) (2021)
- [WIKIGENBENCH: Exploring Full-length Wikipedia Generation under Real-World Scenario](https://mlsanity.com/paper/2402.18264/) (2024)
- [Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition](https://mlsanity.com/paper/2402.18873/) (2024)
- [Enhancing Long Document Long Form Summarisation with Self-Planning](https://mlsanity.com/paper/2512.17179/) (2025)
- [FRACTAL: Fine-Grained Scoring from Aggregate Text Labels](https://mlsanity.com/paper/2404.04817/) (2024)
- [Leveraging Entailment Judgements in Cross-Lingual Summarisation](https://mlsanity.com/paper/2408.00675/) (2024)

## Abstract (arXiv)

Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.