Papers by Satoshi Kosugi
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)
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| Challenge: | Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training. |
| Approach: | They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution. |
| Outcome: | The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones. |
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation (2024.findings-naacl)
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| Challenge: | Existing methods to extract word embeddings from training datasets are not efficient for training other models. |
| Approach: | They propose a method to distill a training dataset into a textual model by combining a small number of informative synthetic samples. |
| Outcome: | The proposed method outperforms existing methods on training datasets and language models. |
Live Football Commentary System Providing Background Information (2025.acl-demo)
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Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information. |
| Approach: | They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches. |
| Outcome: | The proposed system generates utterances with background information and play-by-play commentary for football matches. |