Papers by Jai-Eun Kim
Title-based Extractive Summarization via MRC Framework (2024.lrec-main)
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| Challenge: | Existing studies on extractive summarization focus on scoring and selecting summary sentences . existing models tend to select generalized sentences while overlooking the overall content of a document. |
| Approach: | They propose a machine reading comprehension framework for extractive summarization by setting a query as the title. |
| Outcome: | The proposed framework outperforms existing models on long and short summaries in Korean and English . it can consider the semantic coherence and relevance of summary sentences in relation to the overall content . |
Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
| Outcome: | The proposed methods outperform BERT-based models in nested NER tasks . however, they do not outperformed the existing models on the GENIA dataset . |