GenAI4ELab/papercli-papers huggingface dataset:
Training of LLM-Based List-Wise Multilingual Reranker
Authors: Hao Yu; David Ifeoluwa Adelani
Venue: EMNLP 2025 (Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025))

Abstract: Multilingual retrieval-augmented generation (MRAG) systems heavily rely on robust Information Retrieval (IR). Reranking as a key component optimizes the initially retrieved document set to present the most pertinent information to the generative model, addressing context limitations and minimizing hallucinations. We propose an approach that trains Large Language Models (LLMs) as multilingual listwise rerankers through supervised fine-tuning (SFT) on a diverse mixture of multilingual and extended English ranking examples, and enhancing reasoning capabilities through Direct Preference Optimization (DPO) from translated task-specific reasoning processes. Experiments demonstrate that the approach improves accuracy@5 by 20-30% across all six high- mediumand low-resource languages compared to the BM25. The posted training 1B models achieve comparable performance to 7B baseline models while enabling faster inference. Finally, we investigate the effectiveness of different reasoning strategies in DPO with crosslingual and monolingual thinking processes.
LLM-as-a-Judge Failures at Automating the Identification of Poor Quality Outputs in Free-Form Texts
Authors: Zongxia Li; Xiyang Wu; Ishani Mondal; Alexa Siu; Jordan Lee Boyd-Graber; Ani Nenkova
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Large language models (LLMs) such as GPT-4, Claude and LLaMA are routinely used to evaluate long-form text generated by language models. We study the ability of these models to identify low quality texts, an increasingly rare subset of output which is of great interest to pinpoint during development. We present experiments with a panel of LLM judges, and crowd-sourced approximations of reference judgments. Pinpointing sub-par outputs is a difficult task for both models and crowdworkers, with models doing overall better. Moreover, unlike findings in prior work on factoid question answering, panels of cheaper models do not agree as well with high quality developer judgments of low quality as panels of frontier models. We present qualitative and quantitative analysis of the relative strengths of models in the panel, gleaning insights why they yield better results over a single model.
Hierarchical Attention Adapter for Abstractive Dialogue Summarization
Authors: Raymond Li; Chuyuan Li; Gabriel Murray; Giuseppe Carenini
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Dialogue summarization is still a very challenging task even for large language models (LLMs). On the one hand, some previous approaches have pre-trained language models specifically for dialogue understanding and summarization, but they have been limited to relatively small models. On the other hand, other works have tried to directly exploit the dialogue semantics and discourse structures in their modeling effort, but by construction, they require access to those structures, which is in itself a largely unsolved problem. In this paper, we synergistically combine these two ideas in an approach that can be seamlessly integrated into the decoder-only architecture adopted by the most state-of-the-art LLMs. In particular, our novel solution leverages the parameter-efficient fine-tuning (PEFT) paradigm to model the hierarchical structure of dialogues, where input sequences are naturally segmented into dialogue turns, and then fine-tune the model for abstractive summarization. From experiments on two datasets, we find that Hierarchical Attention Adapter outperforms all baseline adapter methods on SummScreen, where our approach can also be combined with LoRA to achieve the best performance on SamSum.
CS-Sum: A Benchmark for Code-Switching Dialogue Summarization and the Limits of Large Language Models
Authors: Sathya Krishnan Suresh; Tanmay Surana; Lim Zhi Hao; Eng Siong Chng
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Code-switching (CS) poses a significant challenge for Large Language Models (LLMs), yet its comprehensibility remains underexplored in LLMs. We introduce CS-Sum, to evaluate the comprehensibility of CS by the LLMs through CS dialogue to English summarization. CS-Sum is the first benchmark for CS dialogue summarization across Mandarin-English (EN-ZH), Tamil-English (EN-TA), and Malay-English (EN-MS), with 900-1300 human-annotated dialogues per language pair. Evaluating ten LLMs, including open and closed-source models, we analyze performance across few-shot, translate-summarize, and fine-tuning (LoRA, QLoRA on synthetic data) approaches. Our findings show that though the scores on automated metrics are high, LLMs make subtle mistakes that alter the complete meaning of the dialogue. To this end, we introduce 3 most common type of errors that LLMs make when handling CS input. Error rates vary across CS pairs and LLMs, with some LLMs showing more frequent errors on certain language pairs, underscoring the need for specialized training on code-switched data.
Beyond Paraphrasing: Analyzing Summarization Abstractiveness and Reasoning
Authors: Nathan Zeweniuk; Ori Ernst; Jackie CK Cheung
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: While there have been many studies analyzing the ability of LLMs to solve problems through reasoning, their application of reasoning in summarization remains largely unexamined. This study explores whether reasoning is essential to summarization by investigating three questions: (1) Do humans frequently use reasoning to generate new summary content? (2) Do summarization models exhibit the same reasoning patterns as humans? (3) Should summarization models integrate more complex reasoning abilities? Our findings reveal that while human summaries often contain reasoning-based information, system-generated summaries rarely contain this same information. This suggests that models struggle to effectively apply reasoning, even when it could improve summary quality. We advocate for the development of models that incorporate deeper reasoning and abstractiveness, and we release our annotated data to support future research.
Improving Aspect-Based Summarization via Contrastive Learning with Anchored Negative Examples
Authors: Elizabeth Palmieri; Yangfeng Ji
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Text summarization helps users manage information overload, but traditional methods can be cumbersome when seeking specific details within a document. Aspect-based text summarization addresses this by using a query to guide which information should be summarized. However, distinguishing relevant from irrelevant information for a given aspect remains challenging in LLM-based summarization models. In this work, we propose utilizing contrastive learning to encourage LLMs to focus on aspect-related signals during training. We further design two variants of the learning algorithm, aspect-anchored and summary-anchored, corresponding to the strategies used in constructing negative examples. Evaluation with two representative LLM families (Llama 2 and Pythia) and two benchmark datasets (AnyAspect and CovidET) demonstrates the proposed methods’ strong performance compared to their supervised fine-tuning and zero-shot counterparts, highlighting contrastive learning as a promising direction for aspect-based text summarization.
REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Authors: Nannan Huang; Haytham M. Fayek; Xiuzhen Zhang
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Individuals express diverse opinions, a fair summary should represent these viewpoints comprehensively.Previous research on fairness in opinion summarisation using large language models (LLMs) relied on hyperparameter tuning or providing ground truth distributional information in prompts. However, these methods face practical limitations: end-users rarely modify default model parameters, and accurate distributional information is often unavailable. Building upon cognitive science research demonstrating that frequency-based representations reduce systematic biases in human statistical reasoning by making reference classes explicit and reducing cognitive load, this study investigates whether frequency framed prompting (REFER) can similarly enhance fairness in LLM opinion summarisation. Through systematic experimentation with different prompting frameworks, we adapted techniques known to improve human reasoning to elicit more effective information processing in language models compared to abstract probabilistic representations. Our results demonstrate that REFER enhances fairness in language models when summarising opinions. This effect is particularly pronounced in larger language models and using stronger reasoning instructions.
DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization
Authors: Xue-Yong Fu; Elena Khasanova; Md Tahmid Rahman Laskar; Harsh Saini; Shashi Bhushan Tn
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Large language models (LLMs) have achieved impressive performance in text summarization, yet their performance often falls short when applied to specialized domains that differ from their original pre-training distribution. While fine-tuning can improve summarization quality, it typically relies on costly and scarce high-quality labeled data. In this work, we explore continual pre-training as a scalable, self-supervised approach to adapt LLMs for downstream summarization tasks, particularly in the context of noisy real-world conversation transcripts. We conduct extensive experiments using large-scale, unlabeled business conversation data to investigate whether continual pre-training enhances model capabilities in conversational summarization. Our results demonstrate that continual pre-training yields substantial gains in both in-domain and out-of-domain summarization benchmarks, while maintaining strong generalization and robustness. We also analyze the effects of data selection strategies, providing practical guidelines for applying continual pre-training in summarization-focused industrial applications.
From Keyterms to Context: Exploring Topic Description Generation in Scientific Corpora
Authors: Pierre Achkar; Satiyabooshan Murugaboopathy; Anne Kreuter; Tim Gollub; Martin Potthast; Yuri Campbell
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Topic models represent topics as ranked term lists, which are often hard to interpret in scientific domains. We explore Topic Description for Scientific Corpora, an approach to generating structured summaries for topic-specific document sets. We propose and investigate two LLM-based pipelines: Selective Context Summarisation (SCS), which uses maximum marginal relevance to select representative documents; and Compressed Context Summarisation (CCS), a hierarchical approach that compresses document sets through iterative summarisation. We evaluate both methods using SUPERT and multi-model LLM-as-a-Judge across three topic modeling backbones and three scientific corpora. Our preliminary results suggest that SCS tends to outperform CCS in quality and robustness, while CCS shows potential advantages on larger topics. Our findings highlight interesting trade-offs between selective and compressed strategies for topic-level summarisation in scientific domains. We release code and data for two of the three datasets.
HalluTree: Explainable Multi-Hop Hallucination Detection for Abstractive Summarization
Authors: Daniel Orshansky; Oskar Oomen; Naaisha Agarwal; Ryan Lagasse
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Black-box verifiers for abstractive summaries often struggle with complex claims that require multi-hop reasoning, and they typically provide a single verdict without an interpretable rationale. As a result, it becomes difficult to understand or audit their failures. We address this with HalluTree, a framework that models verification as an interpretable claim tree. HalluTree first decomposes summaries into subclaims, classifying each into two types – extractive (directly verifiable against evidence) or inferential (requiring reasoning) – which follow distinct verification paths. Extractive claims are robustly verified against evidence using an ensemble of lightweight NLI models. Crucially, inferential claims trigger a process that generates a natural program – an explicit reasoning chain that integrates supporting evidence and logical steps – which is then executed to determine the claim’s validity. Evaluation on the LLM-AggreFact benchmark demonstrates HalluTree’s effectiveness: it achieves performance competitive with top-tier black-box models, including Bespoke-MiniCheck, while providing transparent and auditable reasoning programs for every inferential judgment. This combination of competitive accuracy and high interpretability offers a significant advance over opaque, single-classification verifiers. We will publically release code, data, prompts, and other artifacts upon acceptance.
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing
Authors: Juntai Cao; Xiang Zhang; Raymond Li; Jiaqi Wei; Chuyuan Li; Shafiq Joty; Giuseppe Carenini
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Recent advances in test-time scaling have shown promising results in improving large language model performance through strategic computation allocation during inference. While this approach has demonstrated strong improvements in reasoning tasks, its application to natural language generation tasks, particularly summarization, remains unexplored.Among all of the generation tasks, multi-document summarization (MDS) presents unique challenges by requiring models to extract and synthesize essential information across multiple lengthy documents. Unlike reasoning tasks, MDS demands a more complicated approach to prompt design and ensemble methods, as no single “best-overall” prompt can satisfy diverse summarization requirements. The inherent diversity in summarization needs necessitates exploring how different prompting strategies can be systematically combined to improve performance.We propose a novel framework that harnesses prompt diversity to enhance MDS performance. Our approach generates multiple candidate summaries using carefully designed prompt variations, then ensemble them through sophisticated aggregation methods to produce refined summaries. This prompt diversity enables models to capture different aspects and perspectives of the source documents, leading to more comprehensive and higher-quality summaries. To evaluate our method effectively, we also introduce two new LLM-based metrics: the Preference Alignment Score (PAS) and LLM Atom-Content-Unit score (LLM-ACU), which assess summary quality while addressing the positional bias inherent in automatic evaluations performed by LLMs.Our experiments demonstrate that leveraging prompt diversity significantly enhances summary quality, while also revealing the practical scaling boundaries for MDS tasks.
Bridging Multimodal and Video Summarization: A Unified Survey
Authors: Haopeng Zhang
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Multimodal summarization (MMS) and video summarization (VS) have traditionally evolved in separate communities—natural language processing (NLP) and computer vision (CV), respectively. MMS focuses on generating textual summaries from inputs such as text, images, or audio, while VS emphasizes selecting key visual content. With the recent rise of vision-language models (VLMs), these once-disparate tasks are converging under a unified framework that integrates visual and linguistic understanding.In this survey, we provide a unified perspective that bridges MMS and VS. We formalize the task landscape, review key datasets and evaluation metrics, and categorize major modeling approaches into new taxonomy. In addition, we highlight core challenges and outline future directions toward building general-purpose multimodal summarization systems. By synthesizing insights from both NLP and CV communities, this survey aims to establish a coherent foundation for advancing this rapidly evolving field.
AdvSumm: Adversarial Training for Bias Mitigation in Text Summarization
Authors: Mukur Gupta; Nikhil Reddy Varimalla; Nicholas Deas; Melanie Subbiah; Kathleen McKeown
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Large Language Models (LLMs) have achieved impressive performance in text summarization and are increasingly deployed in real-world applications. However, these systems often inherit associative and framing biases from pre-training data, leading to inappropriate or unfair outputs in downstream tasks. In this work, we present AdvSumm (Adversarial Summarization), a domain-agnostic training framework designed to mitigate bias in text summarization through improved generalization. Inspired by adversarial robustness, AdvSumm introduces a novel Perturber component that applies gradient-guided perturbations at the embedding level of Sequence-to-Sequence models, enhancing the model’s robustness to input variations. We empirically demonstrate that AdvSumm effectively reduces different types of bias in summarization—specifically, name-nationality bias and political framing bias—without compromising summarization quality. Compared to standard transformers and data augmentation techniques like back-translation, AdvSumm achieves stronger bias mitigation performance across benchmark datasets.
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims
Authors: Delip Rao; Weiqiu You; Eric Wong; Chris Callison-Burch
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with an estimated 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. We present two focused subsets: NSF-SciFy-MatSci with 114,000 claims from materials science awards, and NSF-SciFy-20K with 135,000 claims across five NSF directorates. Using zero-shot prompting, we develop a scalable approach for joint extraction of scientific claims and investigation proposals. We demonstrate the dataset’s utility through three downstream tasks: non-technical abstract generation, claim extraction, and investigation proposal extraction. Fine-tuning language models on our dataset yields substantial improvements, with relative gains often exceeding 100%, particularly for claim and proposal extraction tasks. Our error analysis reveals that extracted claims exhibit high precision but lower recall, suggesting opportunities for further methodological refinement. NSF-SciFy enables new research directions in large-scale claim verification, scientific discovery tracking, and meta-scientific analysis.
QA-prompting: Improving Summarization with Large Language Models using Question-Answering
Authors: Neelabh Sinha
Venue: EMNLP 2025 (Proceedings of The 5th New Frontiers in Summarization Workshop)

Abstract: Language Models (LMs) have revolutionized natural language processing, enabling high-quality text generation through prompting and in-context learning. However, models often struggle with long-context summarization due to positional biases, leading to suboptimal extraction of critical information. There are techniques to improve this with fine-tuning, pipelining, or using complex techniques, which have their own challenges. To solve these challenges, we propose QA-prompting - a simple prompting method for summarization that utilizes question-answering as an intermediate step prior to summary generation. Our method extracts key information and enriches the context of text to mitigate positional biases and improve summarization in a single LM call per task without requiring fine-tuning or pipelining. Experiments on multiple datasets belonging to different domains using ten state-of-the-art pre-trained models demonstrate that QA-prompting outperforms baseline and other state-of-the-art methods, achieving up to 29% improvement in ROUGE scores. This provides an effective and scalable solution for summarization and highlights the importance of domain-specific question selection for optimal performance.
Tracing Definitions: Lessons from Alliance Contracts in the Biopharmaceutical Industry
Authors: Maximilian Kreutner; Doerte Leusmann; Florian Lemmerich; Carolin Haeussler
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Definitions in alliance contracts play a critical role in shaping agreements, yet they can also lead to costly misunderstandings. This is exemplified by the multimillion-dollar AstraZeneca-Euopean Commission (EC) dispute, where the interpretation of ‘best reasonable effort’ became the focal point of contention. In this interdisciplinary study, we leverage natural language processing (NLP) to systematically analyze patterns in the definitions included in alliance contracts. More specifically, we categorize the content of definitions into topics, identify common terms versus outliers that are semantically dissimilar and infrequently used, and track how definitions evolve over time. Analyzing a dataset of 380,131 definitions from 12,468 alliance contracts in the biopharmaceutical industry, we distinguish that definitions span legal, technological, and social topics, with social terms showing the highest dissimilarity across contracts. Using dynamic topic modeling, we explore how the content of definitions has shifted over two decades (2000–2020) and identify prevalent trends suggesting that contractual definitions reflect broader economic contexts. Notably, our results reveal that the AstraZeneca-EC dispute arose from an outlier—a highly unusual definition—that could have been flagged using NLP. Overall, these findings highlight the potential of data-driven approaches to uncover patterns in alliance contracts.
The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
Authors: Shenzhe Zhu; Jiao Sun; Yi Nian; Tobin South; Alex Pentland; Jiaxin Pei
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we investigate a future setting where both consumers and merchants authorize AI agents to automate the negotiations and transactions in consumer settings. We aim to address two questions: (1) Do different LLM agents exhibit varying performances when making deals on behalf of their users? (2) What are the potential risks when we use AI agents to fully automate negotiations and deal-making in consumer settings? We designed an experimental framework to evaluate AI agents’ capabilities and performance in real-world negotiation and transaction scenarios, and experimented with a range of open-source and closed-source LLMs. Our analysis reveals that deal-making with LLM agents in consumer settings is an inherently imbalanced game: different AI agents have large disparities in obtaining the best deals for their users. Furthermore, we found that LLMs’ behavioral anomaly might lead to financial loss when deployed in real-world decision-making scenarios, such as overspending or making unreasonable deals. Our findings highlight that while automation can enhance transactional efficiency, it also poses nontrivial risks to consumer markets. Users should be careful when delegating business decisions to LLM agents.