# EntSUM: A Data Set for Entity-Centric Summarization

arXiv: https://arxiv.org/abs/2204.02213 · HTML: https://mlsanity.com/paper/2204.02213/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Extractive Opinion Summarization in Quantized Transformer Spaces](https://mlsanity.com/paper/2012.04443/) (2020)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Content Selection in Deep Learning Models of Summarization](https://mlsanity.com/paper/1810.12343/) (2018)
- [Aspect-Controllable Opinion Summarization](https://mlsanity.com/paper/2109.03171/) (2021)
- [Informative and Controllable Opinion Summarization](https://mlsanity.com/paper/1909.02322/) (2019)
- [Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models](https://mlsanity.com/paper/2003.13028/) (2020)
- [Decision-Focused Summarization](https://mlsanity.com/paper/2109.06896/) (2021)
- [iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration](https://mlsanity.com/paper/2109.11621/) (2021)
- [Subjective Bias in Abstractive Summarization](https://mlsanity.com/paper/2106.10084/) (2021)

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://mlsanity.com/paper/2309.04269/) (2023)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [MACSum: Controllable Summarization with Mixed Attributes](https://mlsanity.com/paper/2211.05041/) (2022)
- [NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization](https://mlsanity.com/paper/2302.05574/) (2023)
- [Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?](https://mlsanity.com/paper/2401.10415/) (2024)
- [GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres](https://mlsanity.com/paper/2401.17974/) (2024)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://mlsanity.com/paper/2402.10554/) (2024)
- [Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review](https://mlsanity.com/paper/2602.11173/) (2026)
- [MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization](https://mlsanity.com/paper/2406.03479/) (2024)
- [BookWorm: A Dataset for Character Description and Analysis](https://mlsanity.com/paper/2410.10372/) (2024)
- [Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization](https://mlsanity.com/paper/2212.10819/) (2022)
- [GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization](https://mlsanity.com/paper/2306.11256/) (2023)
- [Leveraging Contextual Information for Effective Entity Salience Detection](https://mlsanity.com/paper/2309.07990/) (2023)
- [Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search](https://mlsanity.com/paper/2509.26435/) (2025)
- [Controllable Topic-Focused Abstractive Summarization](https://mlsanity.com/paper/2311.06724/) (2023)
- [Scalable Detection of Salient Entities in News Articles](https://mlsanity.com/paper/2405.20461/) (2024)
- [Exploring Iterative Controllable Summarization with Large Language Models](https://mlsanity.com/paper/2411.12460/) (2024)
- [Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods](https://mlsanity.com/paper/2206.04317/) (2022)
- [CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models](https://mlsanity.com/paper/2510.07177/) (2025)

## Abstract (arXiv)

Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control. We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set.