# What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research

arXiv: https://arxiv.org/abs/2012.07619 · HTML: https://mlsanity.com/paper/2012.07619/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://mlsanity.com/paper/2305.09022/) (2023)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patterns](https://mlsanity.com/paper/2504.15815/) (2025)
- [PerSEval: Assessing Personalization in Text Summarizers](https://mlsanity.com/paper/2407.00453/) (2024)
- [Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!](https://mlsanity.com/paper/2410.00149/) (2024)
- [Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection](https://mlsanity.com/paper/2504.15392/) (2025)
- [Query-Focused Extractive Summarization for Sentiment Explanation](https://mlsanity.com/paper/2509.11989/) (2025)
- [Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers](https://mlsanity.com/paper/2510.10082/) (2025)

## Abstract (arXiv)

Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many. Despite this development, there is little research that meaningfully investigates how the current research focus in automatic summarization aligns with users' needs. To bridge this gap, we propose a survey methodology that can be used to investigate the needs of users of automatically generated summaries. Importantly, these needs are dependent on the target group. Hence, we design our survey in such a way that it can be easily adjusted to investigate different user groups. In this work we focus on university students, who make extensive use of summaries during their studies. We find that the current research directions of the automatic summarization community do not fully align with students' needs. Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.