# How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

arXiv: https://arxiv.org/abs/1909.08837 · HTML: https://mlsanity.com/paper/1909.08837/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Towards Improving Faithfulness in Abstractive Summarization](https://mlsanity.com/paper/2210.01877/) (2022)
- [Target-aware Abstractive Related Work Generation with Contrastive Learning](https://mlsanity.com/paper/2205.13339/) (2022)
- [RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization](https://mlsanity.com/paper/2109.07943/) (2021)
- [Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2108.02859/) (2021)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [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)
- [POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events](https://mlsanity.com/paper/2212.02629/) (2022)
- [Scientific Paper Extractive Summarization Enhanced by Citation Graphs](https://mlsanity.com/paper/2212.04214/) (2022)
- [Automatic Document Sketching: Generating Drafts from Analogous Texts](https://mlsanity.com/paper/2106.07192/) (2021)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data](https://mlsanity.com/paper/2207.05194/) (2022)
- [RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity](https://mlsanity.com/paper/2501.14113/) (2025)

## Abstract (arXiv)

Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.