Papers with meeting summarization
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization (2024.emnlp-industry)
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| Challenge: | Existing LLMs require a new call to the inference endpoint/API for each new query . repeated calls to the endpoints/AP Is expensive and impractical for many real-world use cases. |
| Approach: | They compare the performance of various LLMs for query-based meeting summarization . they find that combining queries for the same context in a single prompt can be used to minimize repeated calls. |
| Outcome: | The proposed approach reduces the number of calls to the inference endpoints/APIs in meeting summarization tasks. |
DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have been used in real-world industrial scenarios for various natural language processing tasks, but their high inference cost makes their deployment impractical, necessitating the use of smaller models. |
| Approach: | They propose a continual pre-training technique that generates diverse task instructions and responses via reading comprehension on conversation transcripts, enabling better instruction generalization. |
| Outcome: | The proposed technique improves small LLMs’ domain adaptability for business conversational tasks, compared with traditional methods that rely on next-token prediction. |
What’s Wrong? Refining Meeting Summaries with LLM Feedback (2025.coling-main)
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| Challenge: | Existing methods for meeting summarization are limited and lack the robustness and context-based accuracy needed to maintain relevance. |
| Approach: | They propose a multi-LLM correction approach for meeting summarization using a two-phase process that mimics the human review process: mistake identification and summary refinement. |
| Outcome: | The proposed approach improves the quality of a given meeting summarization measured by relevance, informativeness, conciseness, and coherence. |
VCSUM: A Versatile Chinese Meeting Summarization Dataset (2023.findings-acl)
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| Challenge: | Compared to news and chat summarization, meeting summarizing is decelerated by the limited data. |
| Approach: | They propose a Chinese meeting summarization dataset that provides annotations for each transcript and a set of benchmark models to facilitate further research. |
| Outcome: | The proposed model can be used to summarize the content of meeting transcripts in Chinese. |
How Domain Terminology Affects Meeting Summarization Performance (2020.coling-main)
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| Challenge: | Existing methods to summarize meetings using domain terminology are understudied . jargon terms are used to identify salient utterances from transcripts . |
| Approach: | They propose to use jargon terms to identify salient utterances from transcribed meetings to generate meeting minutes. |
| Outcome: | The proposed system generates minutes from transcribed meetings by identifying salient utterances from transcripts. |
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (2023.acl-long)
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| Challenge: | Existing methods for meeting summarization use extract-thengenerate method to select "salient" contents . extract-thangenerates method typically selects "selected" content in a distantly supervised manner . |
| Approach: | They propose a novel extractor-guided method to generate a summary from evidence sentences that "explain" a meeting summary. |
| Outcome: | The proposed method outperforms existing methods with gains of up to 3.13 in the ROUGE-1 score. |
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)
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| Challenge: | Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information. |
| Approach: | They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary. |
| Outcome: | The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets. |
Summarizing Speech: A Comprehensive Survey (2025.emnlp-main)
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Fabian Retkowski, Maike Züfle, Andreas Sudmann, Dinah Pfau, Shinji Watanabe, Jan Niehues, Alexander Waibel
| Challenge: | Podcasts and other audiovisual content are becoming more and more a part of everyday communication and the digital age is changing from text to voice. |
| Approach: | They synthesize the current state of the field and highlight the need for realistic evaluation benchmarks and multilingual datasets. |
| Outcome: | The proposed frameworks are based on evaluation protocols and datasets and highlight the need for realistic benchmarks and multilingual datasets. |