Papers by Quan Sheng
Automatic, Meta and Human Evaluation for Multimodal Summarization with Multimodal Output (2024.naacl-long)
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| Challenge: | Multimodal summarization with multimodal output (MSMO) has attracted increasing research interest . evaluation is an emerging yet underexplored research topic . |
| Approach: | They propose a framework that studies three research questions of MSMO evaluation . they propose an automatic evaluation metric and a meta-evaluation benchmark dataset . |
| Outcome: | The proposed evaluation metric and human-annotated meta-evaluation benchmark are used to assess the quality of evaluation metrics and show the framework is effective. |
Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization (2024.findings-eacl)
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| Challenge: | Existing methods for contrastive learning rely on manual negative examples and are poor in quality and adaptability during training. |
| Approach: | They propose a framework that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization by combining a negative example network and a representation network. |
| Outcome: | The proposed approach eliminates the need for manual negative example design and improves on two benchmark datasets. |
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation (2024.emnlp-main)
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| Challenge: | Objective questions such as fill-in-the-blank and multiple-choice require examinees to select one valid answer from a set of invalid options. |
| Approach: | They examine distractor generation tasks, datasets, methods, and evaluation metrics for English objective questions. |
| Outcome: | The proposed task is based on fill-in-the-blank and multiple choice questions and is widely utilized in educational settings across various domains and subjects. |