# Controlling the Amount of Verbatim Copying in Abstractive Summarization

arXiv: https://arxiv.org/abs/1911.10390 · HTML: https://mlsanity.com/paper/1911.10390/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Annotating and Modeling Fine-grained Factuality in Summarization](https://mlsanity.com/paper/2104.04302/) (2021)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [Training Dynamics for Text Summarization Models](https://mlsanity.com/paper/2110.08370/) (2021)
- [Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search](https://mlsanity.com/paper/2203.08436/) (2022)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [How well do you know your summarization datasets?](https://mlsanity.com/paper/2106.11388/) (2021)
- [Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2108.02859/) (2021)
- [Salience Allocation as Guidance for Abstractive Summarization](https://mlsanity.com/paper/2210.12330/) (2022)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [MACSum: Controllable Summarization with Mixed Attributes](https://mlsanity.com/paper/2211.05041/) (2022)
- [HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models](https://mlsanity.com/paper/2110.04400/) (2021)
- [Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution](https://mlsanity.com/paper/2106.01518/) (2021)
- [May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation](https://mlsanity.com/paper/2112.10360/) (2021)
- [Automatic Generation of Factual News Headlines in Finnish](https://mlsanity.com/paper/2212.02170/) (2022)

## Abstract (arXiv)

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.