# Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

arXiv: https://arxiv.org/abs/2311.11103 · HTML: https://mlsanity.com/paper/2311.11103/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 71 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Factual Error Correction for Abstractive Summarization Models](https://mlsanity.com/paper/2010.08712/) (2020)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Multi-Fact Correction in Abstractive Text Summarization](https://mlsanity.com/paper/2010.02443/) (2020)
- [Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization](https://mlsanity.com/paper/2105.12544/) (2021)
- [Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations](https://mlsanity.com/paper/2109.12174/) (2021)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications](https://mlsanity.com/paper/2205.06828/) (2022)
- [Focus Attention: Promoting Faithfulness and Diversity in Summarization](https://mlsanity.com/paper/2105.11921/) (2021)
- [Does Pretraining for Summarization Require Knowledge Transfer?](https://mlsanity.com/paper/2109.04953/) (2021)
- [CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization](https://mlsanity.com/paper/2108.13139/) (2021)
- [AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms](https://mlsanity.com/paper/2306.03280/) (2023)
- [EmailSum: Abstractive Email Thread Summarization](https://mlsanity.com/paper/2107.14691/) (2021)
- [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022)
- [HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://mlsanity.com/paper/2203.10741/) (2022)
- [Unpacking the Expressed Consequences of AI Research in Broader Impact Statements](https://mlsanity.com/paper/2105.04760/) (2021)
- [CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes](https://mlsanity.com/paper/2106.02524/) (2021)
- [Dialect Diversity in Text Summarization on Twitter](https://mlsanity.com/paper/2007.07860/) (2020)
- [AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements](https://mlsanity.com/paper/2111.01705/) (2021)
- [StreamHover: Livestream Transcript Summarization and Annotation](https://mlsanity.com/paper/2109.05160/) (2021)
- [Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics](https://mlsanity.com/paper/2011.04096/) (2020)
- [MultiOpEd: A Corpus of Multi-Perspective News Editorials](https://mlsanity.com/paper/2106.02725/) (2021)
- [Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization](https://mlsanity.com/paper/2106.01317/) (2021)
- [Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization](https://mlsanity.com/paper/2101.12406/) (2021)
- [A Survey on Neural Network-Based Summarization Methods](https://mlsanity.com/paper/1804.04589/) (2018)
- [Encoded Summarization: Summarizing Documents into Continuous Vector Space for Legal Case Retrieval](https://mlsanity.com/paper/2309.08187/) (2023)

## Built on by (most cited first, 2 of 2)

- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Bridging the Data Gap: Creating a Hindi Text Summarization Dataset from the English XSUM](https://mlsanity.com/paper/2601.01543/) (2026)

## Abstract (arXiv)

AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how prevalent such issues are, or when and why these issues are likely to arise, remains limited. Focusing on text summarization -- a common NLP task largely overlooked by the responsible AI community -- we examine research and reporting practices in the current literature. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals. We also discuss current evaluation practices and consider how authors discuss the limitations of both prior work and their own work. Overall, we find that relatively few papers engage with possible stakeholders or contexts of use, which limits their consideration of potential downstream adverse impacts or other responsible AI issues. Based on our findings, we make recommendations on concrete practices and research directions.