Papers by Yusen Zhu
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)
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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train. |
| Approach: | They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets. |
| Outcome: | The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets. |
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)
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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev
| Challenge: | Existing models struggle with summarizing long text due to high memory complexity of the full self-attention. |
| Approach: | They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding. |
| Outcome: | The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX. |
MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)
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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
| Challenge: | Existing work on controllable summarization with mixed attributes lacks designated annotations. |
| Approach: | They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources . |
| Outcome: | The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks . |
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition (2022.findings-acl)
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| Challenge: | Existing studies on ERC focus on context modeling but ignore representation of contextual emotional tendency. |
| Approach: | They propose to use Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two benchmark datasets. |
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)
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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang
| Challenge: | Existing methods to handle long text are limited due to time and memory complexity and limited input lengths. |
| Approach: | They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages . |
| Outcome: | The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset. |