4 accepted papers
This study investigates the effectiveness of Large Language Models (LLMs) for zero-shot keyphrase extraction (KE). We propose and evaluate four prompting strategies: vanilla, role prompting, candidate-based prompting, and hybrid prompting. Experiments conducted on six widely-used KE benchmark datase…
Despite biases in Large Language Models (LLMs) being widely researched, systematic explorations of political biases in news article generation tasks remain underexplored. This study evaluates political bias across seven LLMs by leveraging our PublicViews dataset-extracted from the TwinViews-13K corp
This paper introduces a novel approach called title phrase generation (TPG) for unsupervised keyphrase generation (UKG), leveraging a pseudo label generated from a document title. Previous UKG method extracts all phrases from a corpus to build a phrase bank, then draws candidate absent keyphrases re…
We propose a novel unsupervised keyphrase extraction approach, called SAMRank, which uses only a self-attention map in a pre-trained language model (PLM) to determine the importance of phrases. Most recent approaches for unsupervised keyphrase extraction mainly utilize contextualized embeddings to c…