Juseon-Do — accepted AI-conference papers

2025

Considering Length Diversity in Retrieval-Augmented Summarization

Juseon-Do, Jaesung Hwang, Jingun Kwon, Hidetaka Kamigaito, Manabu Okumura

NAACL 2025findings

This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths because previous methods have not considered length constraints. We propose a Diverse Length-aware Maximal Marginal Relevance (DL-MMR) algorithm to better control summary length…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models

Juseon-Do, Jingun Kwon, Hidetaka Kamigaito, Manabu Okumura

ACL 2024findings

Extractive summarization can produce faithful summaries but often requires additional constraints such as a desired summary length. Traditional sentence compression models do not typically consider the constraints because of their restricted model abilities, which require model modifications for cop…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗