ymoslem/acl-abstracts huggingface dataset:
WisPerMed @ PerAnsSumm 2025: Strong Reasoning Through Structured Prompting and Careful Answer Selection Enhances Perspective Extraction and Summarization of Healthcare Forum Threads (2025)

Healthcare community question-answering (CQA) forums provide multi-perspective insights into patient experiences and medical advice. Summarizations of these threads must account for these perspectives, rather than relying on a single {\textquotedblleft}best{\textquotedblright} answer. This paper presents the participation of the WisPerMed team in the PerAnsSumm shared task 2025, which consists of two sub-tasks: (A) span identification and classification, and (B) perspectivebased summarization. For Task A, encoder models, decoder-based LLMs, and reasoningfocused models are evaluated under finetuning, instruction-tuning, and prompt-based paradigms. The experimental evaluations employing automatic metrics demonstrate that DeepSeek-R1 attains a high proportional recall (0.738) and F1-Score (0.676) in zero-shot settings, though strict boundary alignment remains challenging (F1-Score: 0.196). For Task B, filtering answers by labeling them with perspectives prior to summarization with Mistral-7B-v0.3 enhances summarization. This approach ensures that the model is trained exclusively on relevant data, while discarding non-essential information, leading to enhanced relevance (ROUGE-1: 0.452) and balanced factuality (SummaC: 0.296). The analysis uncovers two key limitations: data imbalance and hallucinations of decoder-based LLMs, with underrepresented perspectives exhibiting suboptimal performance. The WisPerMed team`s approach secured the highest overall ranking in the shared task.
DataHacks at PerAnsSumm 2025: LoRA-Driven Prompt Engineering for Perspective Aware Span Identification and Summarization (2025)

This paper presents the approach of the DataHacks team in the PerAnsSumm Shared Task at CL4Health 2025, which focuses on perspective-aware summarization of healthcare community question-answering (CQA) forums. Unlike traditional CQA summarization, which relies on the best-voted answer, this task captures diverse perspectives, including {\textquoteleft}cause,' {\textquoteleft}suggestion,' {\textquoteleft}experience,' {\textquoteleft}question,' and {\textquoteleft}information.' The task is divided into two subtasks: (1) identifying and classifying perspective-specific spans, and (2) generating perspective-specific summaries. We addressed these tasks using Large Language Models (LLM), fine-tuning it with different low-rank adaptation (LoRA) configurations to balance performance and computational efficiency under resource constraints. In addition, we experimented with various prompt strategies and analyzed their impact on performance. Our approach achieved a combined average score of 0.42, demonstrating the effectiveness of fine-tuned LLMs with adaptive LoRA configurations for perspective-aware summarization.
LMU at PerAnsSumm 2025: LlaMA-in-the-loop at Perspective-Aware Healthcare Answer Summarization Task 2.2 Factuality (2025)

In this paper, we describe our submission for the shared task on Perspective-aware Healthcare Answer Summarization. Our system consists of two quantized models of the LlaMA family, applied across fine-tuning and few-shot settings. Additionally, we adopt the SumCoT prompting technique to improve the factual correctness of the generated summaries. We show that SumCoT yields more factually accurate summaries, even though this improvement comes at the expense of lower performance on lexical overlap and semantic similarity metrics such as ROUGE and BERTScore. Our work highlights an important trade-off when evaluating summarization models.
Lightweight LLM Adaptation for Medical Summarisation: Roux-lette at PerAnsSumm Shared Task (2025)

The PerAnsSumm Shared Task at CL4Health@NAACL 2025 focused on Perspective-Aware Summarization of Healthcare Q/A forums, requiring participants to extract and summarize spans based on predefined perspective categories. Our approach leveraged LLM-based zero-shot prompting enhanced by semantically-similar In-Context Learning (ICL) examples. Using Qwen-Turbo with 20 exemplar samples retrieved through NV-Embed-v2 embeddings, we achieved a mean score of 0.58 on Task A (span identification) and Task B (summarization) mean scores of 0.36 in Relevance and 0.28 in Factuality, finishing 12th on the final leaderboard. Notably, our system achieved higher precision in strict matching (0.20) than the top-performing system, demonstrating the effectiveness of our post-processing techniques. In this paper, we detail our ICL approach for adapting Large Language Models to Perspective-Aware Medical Summarization, analyze the improvements across development iterations, and finally discuss both the limitations of the current evaluation framework and future challenges in modeling this task. We release our code for reproducibility.
AICOE at PerAnsSumm 2025: An Ensemble of Large Language Models for Perspective-Aware Healthcare Answer Summarization (2025)

The PerAnsSumm 2024 shared task at the CL4Health workshop focuses on generating structured, perspective-specific summaries to enhance the accessibility of health-related information. Given a Healthcare community QA dataset containing a question, context, and multiple user-answers, the task involves identifying relevant perspective categories, extracting spans from these perspectives, and generating concise summaries for the extracted spans. We fine-tuned open-source models such as Llama-3.2 3B, Llama-3.1 8B, and Gemma-2 9B, while also experimenting with proprietary models including GPT-4o, o1, Gemini-1.5 Pro, and Gemini-2 Flash Experimental using few-shot prompting. Our best-performing approach leveraged an ensemble strategy, combining span outputs from o1 (CoT) and Gemini-2 Flash Experimental. For overlapping perspectives, we prioritized Gemini. The final spans were summarized using Gemini, preserving the higher classification accuracy of o1 while leveraging Gemini`s superior span extraction and summarization capabilities. This hybrid method secured fourth place on the final leaderboard among 100 participants and 206 submissions.
LTRC-IIITH at PerAnsSumm 2025: SpanSense - Perspective-specific span identification and Summarization (2025)

Healthcare community question-answering (CQA) forums have become popular for users seeking medical advice, offering answers that range from personal experiences to factual information. Traditionally, CQA summarization relies on the best-voted answer as a reference summary. However, this approach overlooks the diverse perspectives across multiple responses. Structuring summaries by perspective could better meet users' informational needs. The PerAnsSumm shared task addresses this by identifying and classifying perspective-specific spans (Task{\_}A) and generating perspective-specific summaries from question-answer threads (Task{\_}B). In this paper, we present our work on the PerAnsSumm shared task 2025 at the CL4Health Workshop, NAACL 2025. Our system leverages the RoBERTa-large model for identifying perspective-specific spans and the BART-large model for summarization. We achieved a Macro-F1 score of 0.9 (90{\%}) and a Weighted-F1 score of 0.92 (92{\%}) for classification. For span matching, our strict matching F1 score was 0.21 (21{\%}), while proportional matching reached 0.68 (68{\%}), resulting in an average Task A score of 0.6 (60{\%}). For Task B, we achieved a ROUGE-1 score of 0.4 (40{\%}), ROUGE-2 of 0.18 (18{\%}), and ROUGE-L of 0.36 (36{\%}). Additionally, we obtained a BERTScore of 0.84 (84{\%}), METEOR of 0.37 (37{\%}), and BLEU of 0.13 (13{\%}), resulting in an average Task B score of 0.38 (38{\%}). Combining both tasks, our system achieved an overall average score of 49{\%} and ranked 6th on the official leaderboard for the shared task.
Abdelmalak at PerAnsSumm 2025: Leveraging a Domain-Specific BERT and LLaMA for Perspective-Aware Healthcare Answer Summarization (2025)

The PerAnsSumm Shared Task - CL4Health@NAACL 2025 aims to enhance healthcare community question-answering (CQA) by summarizing diverse user perspectives. It consists of two tasks: identifying and classifying perspective-specific spans (Task A) and generating structured, perspective-specific summaries from question-answer threads (Task B). The dataset used for this task is the PUMA dataset. For Task A, a COVID-Twitter-BERT model pre-trained on COVID-related text from Twitter was employed, improving the model`s understanding of relevant vocabulary and context. For Task B, LLaMA was utilized in a prompt-based fashion. The proposed approach achieved 9th place in Task A and 16th place overall, with the best proportional classification F1-score of 0.74.
UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning (2025)

We present our approach to the PerAnsSumm Shared Task, which involves perspective span identification and perspective-aware summarization in community question-answering (CQA) threads. For span identification, we adopt ensemble learning that integrates three transformer models through averaging to exploit individual model strengths, achieving an 82.91{\%} F1-score on test data. For summarization, we design a suite of Chain-of-Thought (CoT) prompting strategies that incorporate keyphrases and guide information to structure summary generation into manageable steps. To further enhance summary quality, we apply prompt optimization using the DSPy framework and supervised fine-tuning (SFT) on Llama-3 to adapt the model to domain-specific data. Experimental results on validation and test sets show that structured prompts with keyphrases and guidance improve summaries aligned with references, while the combination of prompt optimization and fine-tuning together yields significant improvement in both relevance and factuality evaluation metrics.
Overview of the PerAnsSumm 2025 Shared Task on Perspective-aware Healthcare Answer Summarization (2025)

This paper presents an overview of the Perspective-aware Answer Summarization (PerAnsSumm) Shared Task on summarizing healthcare answers in Community Question Answering forums hosted at the CL4Health Workshop at NAACL 2025. In this shared task, we approach healthcare answer summarization with two subtasks: (a) perspective span identification and classification and (b) perspective-based answer summarization (summaries focused on one of the perspective classes). Wedefine a benchmarking setup for comprehensive evaluation of predicted spans and generated summaries. We encouraged participants to explore novel solutions to the proposed problem and received high interest in the task with 23 participating teams and 155 submissions. This paper describes the task objectives, the dataset, the evaluation metrics and our findings. We share the results of the novel approaches adopted by task participants, especially emphasizing the applicability of Large Language Models in this perspective-based answer summarization task.
Bridging the Gap: Inclusive Artificial Intelligence for Patient-Oriented Language Processing in Conversational Agents in Healthcare (2025)

Conversational agents (CAs), such as medical interview assistants, are increasingly used in healthcare settings due to their potential for intuitive user interaction. Ensuring the inclusivity of these systems is critical to provide equitable and effective digital health support. However, the underlying technology, models and data can foster inequalities and exclude certain individuals. This paper explores key principles of inclusivity in patient-oriented language processing (POLP) for healthcare CAs to improve accessibility, cultural sensitivity, and fairness in patient interactions. We will outline, how considering the six facets of inclusive Artificial Intelligence (AI) will shape POLP within healthcare CA. Key considerations include leveraging diverse datasets, incorporating gender-neutral and inclusive language, supporting varying levels of health literacy, and ensuring culturally relevant communication. To address these issues, future research in POLP should focus on optimizing conversation structure, enhancing the adaptability of CAs' language and content, integrating cultural awareness, improving explainability, managing cognitive load, and addressing bias and fairness concerns.
The Exception of Humor: Iconicity, Phonemic Surprisal, Memory Recall, and Emotional Associations (2025)

This meta-study explores the relationships between humor, phonemic bigram surprisal, emotional valence, and memory recall. Prior research indicates that words with higher phonemic surprisal are more readily remembered, suggesting that unpredictable phoneme sequences promote long-term memory recall. Emotional valence is another well-documented factor influencing memory, with negative experiences and stimuli typically being remembered more easily than positive ones. Building on existing findings, this study highlights that words with negative associations often exhibit greater surprisal and are easier to recall. Humor, however, presents an exception: while associated with positive emotions, humorous words also display heightened surprisal and enhanced memorability.
Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor (2025)

While Large Language Models (LLMs) have demonstrated impressive natural language understanding capabilities across various text-based tasks, understanding humor has remained a persistent challenge. Humor is frequently multimodal, relying not only on the meaning of the words, but also their pronunciations, and even the speaker`s intonations. In this study, we explore a simple multimodal prompting approach to humor understanding and explanation. We present an LLM with both the text and the spoken form of a joke, generated using an off-the-shelf text-to-speech (TTS) system. Using multimodal cues improves the explanations of humor compared to textual prompts across all tested datasets.
Rule-based Approaches to the Automatic Generation of Puns Based on Given Names in French (2025)

Humor is a cornerstone of human interactions. Because puns and word plays lie in the margins of phonology, syntax and semantics, large language models struggle with their generation. In this paper, we present two versions of a tool designed to create a typical kind of French jokes known as {\textquotedblleft}Monsieur et Madame{\textquotedblright} jokes. We then discuss the main challenges and limitations rule based systems face when creating this kind of puns.
Homophonic Pun Generation in Code Mixed Hindi English (2025)

In this study, we investigate Hinglish{---}a blend of Hindi and English commonly found in informal online communication{---}with a particular focus on automated pun generation. Our work examines the applicability and adaptability of existing English pun generation pipelines to Hinglish. We assess the pun generation capabilities of Large Language Models (LLMs), particularly GPT-3.5. By employing Chain of Thought prompting and Self-Refine techniques, we identify cross-linguistic homophone detection as a central difficulty. To address this, we propose a novel algorithm for cross-lingual homophone identification and develop a Latin-to-Devanagari transliteration module to leverage the widespread use of Latin-script Hindi in online settings. Building on existing frameworks for pun generation, we incorporate our homophone and transliteration modules to improve output quality. Crowd-sourced human evaluations validate the effectiveness of our approach.
Bridging Laughter Across Languages: Generation of Hindi-English Code-mixed Puns (2025)

Puns, as a linguistic phenomenon, hold significant importance in both humor and language comprehension. While extensive research has been conducted in the realm of pun generation in English, there exists a notable gap in the exploration of pun generation within code-mixed text, particularly in Hindi-English code-mixed text. This study addresses this gap by offering a computational method specifically designed to create puns in Hindi-English code-mixed text. In our investigation, we delve into three distinct methodologies aimed at pun generation utilizing pun-alternate word pairs. Furthermore, this novel dataset, HECoP, comprising of 2000 human-annotated sentences serves as a foundational resource for training diverse pun detection models. Additionally, we developed a structured pun generation pipeline capable of generating puns from a single input word without relying on predefined word pairs. Through rigorous human evaluations, our study demonstrates the efficacy of our proposed models in generating code-mixed puns. The findings presented herein lay a solid groundwork for future endeavours in pun generation and computational humor within diverse linguistic contexts.
Testing Humor Theory Using Word and Sentence Embeddings (2025)

A basic prediction of incongruity theory is that semantic scripts in verbal humor should be in a state of incongruity. We test this prediction using a dataset of 1,182 word/phrase pairs extracted from a set of imperfect puns. Incongruity was defined as the cosine distance between their word vector representations. We compare these pun distances against similarity metrics for the pun words against their synonyms, extracted from WordNet. Results indicate a significantly lower degree of similarity between pun words when compared to their synonyms. Our findings support the basic predictions of incongruity theory and provide computational researchers with a baseline metric to model humorous incongruity.
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection (2025)

Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection helping LLMs interpret implied meanings, consider contextual cues, and reflect on discrepancies to identify sarcasm. Using state-of-the-art LLMs such as LLaMA-3-8B, GPT-4o, and Claude 3.5 Sonnet, PMP achieves state-of-the-art performance on GPT-4o on MUStARD and SemEval2018. This study demonstrates that integrating pragmatic reasoning and metacognitive strategies into prompting significantly enhances LLMs' ability to detect sarcasm, offering a promising direction for future research in sentiment analysis.
Can AI Make Us Laugh? Comparing Jokes Generated by Witscript and a Human Expert (2025)

This study compares the funniness of AI-generated jokes and those written by a professional human joke writer, using audience laughter as a direct measure. Prior research has typically relied on numerical ratings, which have limitations. Our findings show that AI-generated jokes elicited as much laughter as human-crafted ones, indicating that advanced AI joke generators can now produce original jokes on par with those of a professional human comedy writer.
Evaluating Human Perception and Bias in AI-Generated Humor (2025)

This paper explores human perception of AI-generated humor, examining biases and the ability to distinguish between human and AI-created jokes. Through a between-subjects user study involving 174 participants, we tested hypotheses on quality perception, source identification, and demographic influences. Our findings reveal that AI-generated jokes are rated comparably to human-generated ones, with source blindness improving AI humor ratings. Participants struggled to identify AI-generated jokes accurately, and repeated exposure led to increased appreciation. Younger participants showed more favorable perceptions, while technical background had no significant impact. These results challenge preconceptions about AI`s humor capabilities and highlight the importance of addressing biases in AI content evaluation. We also suggest pathways for enhancing human-AI creative collaboration and underscore the need for transparency and ethical considerations in AI-generated content.
The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance (2025)

In this position paper, we review the eclectic recent history of academic and artistic works involving computational systems for humor generation, and focus specifically on live performance. We make the case that AI comedy should be evaluated in live conditions, in front of audiences sharing either physical or online spaces, and under real-time constraints. We further suggest that improvised comedy is therefore the perfect substrate for deploying and assessing computational humor systems. Using examples of successful AI-infused shows, we demonstrate that live performance raises three sets of challenges for computational humor generation: 1) questions around robotic embodiment, anthropomorphism and competition between humans and machines, 2) questions around comedic timing and the nature of audience interaction, and 3) questions about the human interpretation of seemingly absurd AI-generated humor. We argue that these questions impact the choice of methodologies for evaluating computational humor, as any such method needs to work around the constraints of live audiences and performance spaces. These interrogations also highlight different types of collaborative relationship of human comedians towards AI tools.
The Algorithm is the Message: Computing as a Humor-Generating Mode (2025)

This position paper starts from the examination of the {\textquotedblleft}Universal Handbook for Political Speeches,{\textquotedblright} a satirical manual created during communist Poland as a modular tool to parody propaganda`s rigid linguistic patterns and its absence of meaning, humorously revealing the absurdity of totalitarian {\textquotedblleft}newspeak.{\textquotedblright} Presented here in English for the first time, the {\textquotedblleft}Handbook{\textquotedblright} is explored as an analog precursor to computational humor systems. More importantly, this artifact shows that humor, rather than being the product of computing, can also arise from a computationalized, combinatorial structure and process. This shifts the focus on computational algorithms and processes as a mode of humor generation, rather than a tool. That is, computing itself{---}with its processes, structure, iteration, and combinatorial logic{---}can be a source of humor, rather than an instrument to fabricate it. The very workings of the machine are what can make us laugh, regardless of what the machine carries or produces. The {\textquotedblleft}Handbook{\textquotedblright} functions here as a spark for reflection, and hopefully a broader discussion, on how this alternative view may impact the evolution of computational humor and its applications at the dawn of the era of artificial general intelligence.
A Brief Overview of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL) (2025)

In this paper, we provide a brief summary of the inaugural workshop on Challenges in Processing South Asian Languages (CHiPSAL) held as part of COLING 2025. The workshop included regular papers, invited keynotes, and shared task papers, fostering a collaborative platform for exploring challenges in processing South Asian languages. The shared task focused on Devanagari-script language understanding, encompassing subtasks on language identification, hate speech detection, and target classification. This workshop series aims to address linguistic and cultural nuances, resource constraints, and orthographic complexities in low-resource South Asian languages while advancing NLP research and promoting multilingual inclusivity.