China

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.

EMNLP 2025

DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization

llms

continual pre-training

large language models

domain adaptation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

workshop paper

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





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Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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.

REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting

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.

Improving Aspect-Based Summarization via Contrastive Learning with Anchored Negative Examples

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.

Beyond Paraphrasing: Analyzing Summarization Abstractiveness and Reasoning

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.

CS-Sum: A Benchmark for Code-Switching Dialogue Summarization and the Limits of Large Language Models

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.

Hierarchical Attention Adapter for Abstractive Dialogue Summarization

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.

LLM-as-a-Judge Failures at Automating the Identification of Poor Quality Outputs in Free-Form Texts

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.

QA-prompting: Improving Summarization with Large Language Models using Question-Answering

The potential of large language models (LLMs) in contract legal risk analysis remains underexplored. In response, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD), we assess 4 proprietary and 15 open-source LLMs. Our results highlight five key findings: (1) Proprietary models outperform open-source models in both correctness and output effectiveness. (2) Larger open-source models generally perform better, though the improvement slows down as models get bigger. (3) Reasoning ("thinking”) mode improves output effectiveness but reduces correctness, likely due to over-complicating simpler tasks. (4) Open-source models generate “no related clause” responses more frequently even when relevant clauses are present. (5) Model quantization speed up inference but at the cost of performance drop, showing the tradeoff between efficiency and accuracy. These findings suggest that while most LLMs perform at a level comparable to junior legal assistants, open-source models require targeted fine-tuning to ensure correctness and effectiveness in high-stakes legal settings. ContractEval offers a solid benchmark to guide future development of legal-domain LLMs.

ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts

Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets.

A Framework to Retrieve Relevant Laws for Will Execution

The Retrieval-Augmented Generation (RAG) systems' performance on Thai legal question answering is still limited, especially for questions requiring extensive, complex legal reasoning. To address these limitations, we introduce a resource-efficient approach that aligns Large Language Models (LLMs) for improved citation accuracy and response quality using Group-Relative Policy Optimization (GRPO). Our proposed method leverages BGE-M3 embeddings as a cost-efficient semantic-similarity reward, significantly reducing computational expenses up to 2.5x compared to an LLM-based reward model. Experiments on the NitiBench benchmark demonstrate substantial improvements: GRPO achieves up to 90% citation-F1 gains relative to the base model and a 31% increase in joint quality metrics over instruction tuning. Crucially, our approach provides a practical and effective solution for enhancing legal LLMs in resource-constrained environments.

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REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting

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AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization

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Next from EMNLP 2025

REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting

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AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization

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