What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research
Not on the map. Why?
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 4.
8 papers.
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 26
- 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
- 2025What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patternscited by 4
- 2024PerSEval: Assessing Personalization in Text Summarizerscited by 3
- 2024Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!cited by 2
- 2025Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detectioncited by 0
- 2025Query-Focused Extractive Summarization for Sentiment Explanationcited by 0
- 2025Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizerscited by 0
- 2025Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers11 Oct 2025
- 2025Query-Focused Extractive Summarization for Sentiment Explanation15 Sep 2025
- 2025What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patterns22 Apr 2025
- 2025Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection21 Apr 2025
- 2024Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!30 Sep 2024
- 2024PerSEval: Assessing Personalization in Text Summarizers29 Jun 2024
- 2024Position: Towards Bidirectional Human-AI Alignment13 Jun 2024
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance15 May 2023