Eunyee Koh


2023

pdf
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Chieh-Yang Huang | Ting-Yao Hsu | Ryan Rossi | Ani Nenkova | Sungchul Kim | Gromit Yeuk-Yin Chan | Eunyee Koh | C Lee Giles | Ting-Hao Huang
Proceedings of the 16th International Natural Language Generation Conference

Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can be refined for better quality. Prior work often treated figure caption generation as a vision-to-language task. In this paper, we show that it can be more effectively tackled as a text summarization task in scientific documents. We fine-tuned PEGASUS, a pre-trained abstractive summarization model, to specifically summarize figure-referencing paragraphs (e.g., “Figure 3 shows...”) into figure captions. Experiments on large-scale arXiv figures show that our method outperforms prior vision methods in both automatic and human evaluations. We further conducted an in-depth investigation focused on two key challenges: (i) the common presence of low-quality author-written captions and (ii) the lack of clear standards for good captions. Our code and data are available at: https://github.com/Crowd-AI-Lab/Generating-Figure-Captions-as-a-Text-Summarization-Task.

2021

pdf
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
Victor Bursztyn | Jennifer Healey | Nedim Lipka | Eunyee Koh | Doug Downey | Larry Birnbaum
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., “It doesn’t look good for a date”), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., “I prefer more romantic”) in order to retrieve reviews pertaining to potentially better recommendations (e.g., “Perfect for a romantic dinner”). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.