Papers by Haojie Zhuang
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. |
Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation (2025.findings-emnlp)
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Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang, Mohammed I. Thanoon, Haojie Zhuang, Behnaz Soltani, Munazza Zaib
| Challenge: | Distractors are used to generate plausible but incorrect options for fill-in-the-blank questions . research studies focus on fine-tuning pre-trained models with data augmentation techniques to generate distractors . |
| Approach: | They propose a model that trains the model to recognize essential semantic features necessary to generate distractors. |
| Outcome: | The proposed model outperforms existing models on two public datasets. |
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. |
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)
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| Challenge: | Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult. |
| Approach: | They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary. |
| Outcome: | The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents. |
The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization (2025.findings-emnlp)
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Mong Yuan Sim, Wei Emma Zhang, Xiang Dai, Biaoyan Fang, Sarbin Ranjitkar, Arjun Burlakoti, Jamie Taylor, Haojie Zhuang
| Challenge: | Current multimodal summarization models often fail to utilize radiology images in summarizing Findings section. |
| Approach: | They conduct a thorough analysis to determine whether current multimodal summarization models can utilize radiology images in summarizing Findings section. |
| Outcome: | The Impression section plays a crucial role in communication between radiologists and physicians. |