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MentSum: A Resource for Exploring Summarization of Mental Health Online Posts
Published 2 Jun 2022 in cs.CL | (2206.00856v1)
Abstract: 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.
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Knowledge Gaps
Knowledge gaps, limitations, and open questions
The following items identify what remains missing, uncertain, or unexplored, and suggest concrete directions for future research:
- Selection bias from requiring author-provided TLDRs: quantify how filtering to posts with TLDRs (via
TL.*DRregex) alters representativeness across subreddits, post lengths, and content types; create and evaluate a complementary set with expert-written summaries for posts lacking TLDRs. - Gold-standard quality uncertainty: systematically annotate TLDRs for fluency, completeness, factuality, and alignment with the post (including cases where TLDRs infer content not explicitly stated) and study how TLDR quality affects training and evaluation outcomes.
- Preprocessing ablations: measure the impact of removing non-ASCII characters (emojis, diacritics), replacing URLs with
@http, and usernames with@useron summary faithfulness, informativeness, and risk-signal coverage; design safer replacements that preserve semantics (e.g., link types, emoji sentiment). - Empirical filtering choices: evaluate how the bigram-redundancy threshold (>3) and compression ratio bounds [2–13] skew the dataset distribution (e.g., topic, style, length), and whether these rules inadvertently remove critical cases; provide transparent ablation results and alternative thresholds.
- Long-input handling: explicitly report truncation strategies and maximum input lengths for models (e.g., BART’s token limits) and quantify information loss; benchmark long-context architectures (LED, Longformer, BigBird) and hierarchical summarization for very long posts.
- Subreddit imbalance: conduct stratified performance analyses per subreddit/condition (e.g., ADHD vs SuicideWatch) to identify where models underperform; design sampling or loss reweighting to mitigate imbalance.
- Domain-specific evaluation: develop mental-health–specific metrics for “actionability,” “risk coverage” (e.g., suicidality, self-harm, substance misuse), “presenting concern,” and “help sought,” and validate them with mental health professionals.
- Real-world utility: run user studies with counselors to measure time saved, accuracy of triage decisions, missed critical information, and fatigue reduction when using model-generated summaries vs original posts.
- Faithfulness and hallucination: quantify factual consistency (e.g., misattributing diagnoses/medications, fabricating events) using question-answering–based metrics (QAEval, QuestEval) and human audits; implement guardrails to reduce hallucinations.
- Safety impacts: assess whether summaries omit or dilute high-risk content (e.g., suicidal intent, plans, means) and whether generated phrasing could cause harm; design safety-aware loss functions or constrained decoding to prioritize risk-critical information.
- Ethical deployment: articulate concrete guidelines and risk mitigations for clinical or peer-support contexts (e.g., disclaimers, escalation protocols, human-in-the-loop review), beyond DUA restrictions; assess potential harms of automated summarization in sensitive settings.
- Cross-platform generalization: test models trained on Reddit TLDRs on private forums (e.g., ReachOut) and other platforms; study domain shift, moderation norms, and content style differences; explore domain adaptation methods.
- Model breadth: benchmark additional summarizers (PEGASUS, T5, UL2, Llama-Instruction variants, and knowledge-infused models) and ensembles; compare pretraining regimes and fine-tuning strategies tailored to mental-health discourse.
- Semantic evaluation: complement ROUGE with BERTScore, MoverScore, and entailment-based metrics; report correlations with human judgments to address paraphrastic and abstractive summaries.
- Structured summaries: design and evaluate schema-driven summarization (e.g., fields for “presenting problem,” “history/diagnoses,” “current risk,” “ask/resources sought”) and train models to reliably populate these slots.
- Temporal robustness: analyze performance across time (2005–2021) to detect concept drift in language and diagnoses; study continual learning or time-aware models to maintain performance on newer posts.
- Privacy leakage: test memorization of sensitive content via canary insertion or exposure audits; explore privacy-preserving training (DP-SGD, PATE) for summarization in this domain.
- Figurative and informal language: evaluate handling of sarcasm, slang, misspellings, code-switching, and emoji; consider retaining or normalizing non-ASCII markers that carry sentiment or intent.
- Multi-condition coverage: measure precision/recall for condition mentions (e.g., anxiety, GAD, agoraphobia) and comorbidity; design objectives that encourage comprehensive coverage without overgeneration.
- Length sensitivity: stratify results by compression ratio and post/TLDR lengths to identify regimes where extractive vs abstractive methods excel; implement length-controlled decoding and analyze trade-offs.
- Link and resource retention: assess whether summarization should preserve references to resources (hotlines, guides) and devise safe abstractions for URLs (e.g., “resource link to crisis support”).
- Duplicate and near-duplicate posts: audit the dataset for duplicated content across Pushshift snapshots and subreddits; deduplicate and measure effects on training stability and evaluation.
- Reproducibility details: publish random seeds, exact split files, and preprocessing scripts; evaluate sensitivity to different splits to ensure comparability across studies.
- Uncertainty signaling: develop methods for models to indicate low confidence or missing information in summaries, prompting counselor review of full posts.
- Ontology-informed methods: test incorporation of mental-health ontologies (e.g., DSM-related term lists) and clinical concept recognizers to improve coverage and reduce hallucinations.
- Style and tone control: investigate controllable summarization to enforce supportive, non-judgmental, and non-triggering language aligned with mental-health best practices.
- Human evaluation scaling: expand annotator pools, include trained clinicians, improve inter-rater reliability, and stratify by subreddit/topic; report confidence intervals and power analyses.
- Cross-dataset transfer: study pretraining on general TLDR datasets (TLDR9+, Newsroom) followed by domain-specific fine-tuning; quantify benefits and risks of transfer learning.
- Integrated risk detection: combine summarization with concurrent risk classification to ensure summaries surface critical signals; evaluate joint or multitask models.
- Vocabulary and OOV effects: examine how train–test vocabulary overlap, slang, and neologisms affect summarization; reconsider non-ASCII removal in this context.
- Noisy-TLDR handling: detect and downweight or rewrite low-quality TLDRs during training; compare training on curated gold summaries authored by independent annotators.
- Oracle bounds: revisit the “up to 3 sentences” limit in extractive oracles; analyze how allowing more sentences changes the upper bound and informs extractive model design.
- Lead bias implications: move beyond bigram-position analysis to quantify how position influences model attention and performance; develop positional-agnostic or global-salience methods.
- Summarization necessity detection: build classifiers to decide when summarization is appropriate (vs already concise posts), and evaluate its impact on overall workflow quality.
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