# MentSum: A Resource for Exploring Summarization of Mental Health Online Posts

arXiv: https://arxiv.org/abs/2206.00856 · HTML: https://mlsanity.com/paper/2206.00856/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [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)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Abstractive Summarization of Reddit Posts with Multi-level Memory Networks](https://mlsanity.com/paper/1811.00783/) (2018)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Better Summarization Evaluation with Word Embeddings for ROUGE](https://mlsanity.com/paper/1508.06034/) (2015)
- [Ontology-Aware Clinical Abstractive Summarization](https://mlsanity.com/paper/1905.05818/) (2019)
- [Revisiting Summarization Evaluation for Scientific Articles](https://mlsanity.com/paper/1604.00400/) (2016)
- [Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization](https://mlsanity.com/paper/2005.00163/) (2020)
- [StreamHover: Livestream Transcript Summarization and Annotation](https://mlsanity.com/paper/2109.05160/) (2021)
- [A Short Review of Ethical Challenges in Clinical Natural Language Processing](https://mlsanity.com/paper/1703.10090/) (2017)
- [TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://mlsanity.com/paper/2110.01159/) (2021)
- [RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses](https://mlsanity.com/paper/1806.07916/) (2018)
- [Triaging Content Severity in Online Mental Health Forums](https://mlsanity.com/paper/1702.06875/) (2017)
- [Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health](https://mlsanity.com/paper/1701.08229/) (2017)
- [Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions](https://mlsanity.com/paper/1804.07253/) (2018)

## Built on by (most cited first, 11 of 11)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://mlsanity.com/paper/2402.09742/) (2024)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://mlsanity.com/paper/2401.16240/) (2024)
- [Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization](https://mlsanity.com/paper/2405.01790/) (2024)
- [Curriculum-guided Abstractive Summarization for Mental Health Online Posts](https://mlsanity.com/paper/2302.00954/) (2023)
- [Domain-specific Guided Summarization for Mental Health Posts](https://mlsanity.com/paper/2411.01485/) (2024)
- [CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis](https://mlsanity.com/paper/2510.11233/) (2025)
- [MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM](https://mlsanity.com/paper/2509.25992/) (2025)

## Abstract (arXiv)

Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health conditions, express their feelings, and seek help from the community and counselors. Some of these platforms, such as Reachout, are dedicated forums where the users register to seek help. Others such as Reddit provide subreddits where the users publicly but anonymously post their mental health distress. Although posts are of varying length, it is beneficial to provide a short, but informative summary for fast processing by the counselors. To facilitate research in summarization of mental health online posts, we introduce Mental Health Summarization dataset, MentSum, containing over 24k carefully selected user posts from Reddit, along with their short user-written summary (called TLDR) in English from 43 mental health subreddits. This domain-specific dataset could be of interest not only for generating short summaries on Reddit, but also for generating summaries of posts on the dedicated mental health forums such as Reachout. We further evaluate both extractive and abstractive state-of-the-art summarization baselines in terms of Rouge scores, and finally conduct an in-depth human evaluation study of both user-written and system-generated summaries, highlighting challenges in this research.