Papers by Youhyun Shin
Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
Fair or Framed? Political Bias in News Articles Generated by LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) have garnered significant attention for applications like news generation and opinion analysis. |
| Approach: | They analyze 10,850 articles and analyze their publicViews dataset to find left-leaning bias persists in generation tasks. |
| Outcome: | The proposed model size and the PublicViews dataset show that left-leaning bias persists in generation tasks and neutral content remains rare even under balanced opinion settings. |
SAMRank: Unsupervised Keyphrase Extraction using Self-Attention Map in BERT and GPT-2 (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for keyphrase extraction use contextualized embeddings to capture semantic relevance between words, sentences, and documents. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses only a self-attention map in a pre-trained language model to determine the importance of phrases. |
| Outcome: | The proposed approach outperforms embedding-based models on three keyphrase extraction datasets. |
Improving Low-Resource Keyphrase Generation through Unsupervised Title Phrase Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for unsupervised keyphrase generation use phrases extracted from document title instead of phrase bank. |
| Approach: | They propose a method for generating pseudo labels from a document title . they use phrases mined from the document title to generate absent keyphrases . |
| Outcome: | The proposed method outperforms existing methods on human-annotated datasets even with fewer labeled data. |