Papers by Longxiang Zhang
Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations (2024.lrec-main)
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| Challenge: | Existing datasets for summarization of medical conversations are limited to conversation-summary pairs . a novel annotation framework is proposed to capture the summarizing process via an annotation task . |
| Approach: | They propose an incremental note generation framework that captures the human summarization process via an annotation task by instructing annotators to first incrementally create a draft note and polish it into a reference note. |
| Outcome: | The proposed framework shows that the human summarization process is much more efficient and accurate than the current method. |
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations (2021.findings-emnlp)
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Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, Matthew R. Gormley
| Challenge: | Using pretrained transformer models for automatically summarizing doctor-patient conversations presents challenges . limited training data, domain shift, long and noisy transcripts, and high target summary variability are challenges compared to human annotators. |
| Approach: | They propose a method for fine-tuning pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. |
| Outcome: | The proposed method surpasses the performance of an average human annotator and the quality of previous published work for the task. |
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)
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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu
| Challenge: | Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along. |
| Approach: | They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks. |
| Outcome: | The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks. |