Papers by Xue-Yong Fu
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective (2023.emnlp-industry)
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| Challenge: | a study examines how to build meeting summarization systems using large language models . closed-source models are generally better in terms of performance, but open-source ones are more advantageous for industrial use . |
| Approach: | They compare closed-source and open-source meeting summarization models for real-world use . they find that closed-sourced models are generally better in terms of performance . however, smaller open-sourced LLMs could still achieve comparable performance if they are open . |
| Outcome: | The proposed model is more efficient for industrial use than closed-source models due to privacy concerns and high cost. |
Developing a Production System for Purpose of Call Detection in Business Phone Conversations (2022.naacl-industry)
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| Challenge: | a commercial system detects Purpose of Call statements in call transcripts . the model is based on a set of rules and a neural model . |
| Approach: | They propose a system to detect Purpose of Call statements in English business call transcripts in real time. |
| Outcome: | The proposed model achieves 88.6 F1 on average in various types of business calls and has low inference time. |
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. |
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations (2022.naacl-industry)
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Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan Tn, Simon Corston-Oliver
| Challenge: | Existing systems that align textual mentions of entities to knowledge bases are difficult to deploy in production environments. |
| Approach: | They propose a neural entity linking system that connects entities in business phone conversations to their corresponding Wikipedia and Wikidata entries. |
| Outcome: | The proposed system improves inference speed and memory consumption while maintaining high accuracy. |
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents (2025.emnlp-industry)
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| Challenge: | Existing knowledge base is time-consuming and deters the adoption of conversational AI systems in contact centers. |
| Approach: | They propose a system that extracts knowledge in the form of question-answer (QA) pairs from historical customeragent conversations to automatically build a knowledge base. |
| Outcome: | The proposed system outperforms larger closed-source LLMs on internal data and achieves above 90% accuracy in answering informationseeking questions. |