34 citations · 56 across the 67 of their papers we have counts for
11 papers · 1 filter
Segment-level Tree Search for Long Meeting Document Summarization
Sangwon Ryu, Heejin Do, Jun Seo +4
Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract info…
Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization
Sangwon Ryu, Yihong Liu, Mingyang Wang +4
Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in dive…
PaT: Planning-after-Trial for Efficient Test-Time Code Generation
Youngsik Yoon, Sungjae Lee, Seockbean Song +3
Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs). However, most e…
Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search
Sangwon Ryu, Heejin Do, Yunsu Kim +2
Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interdependence among attributes makes it ch…
DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
Solee Im, Wonjun Lee, Jinmyeong An +3
We present DeRAGEC, a method for improving Named Entity (NE) correction in Automatic Speech Recognition (ASR) systems. By extending the Retrieval-Augmented Generative Error Correct…
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs
Sungjae Lee, Hoyoung Kim, Jeongyeon Hwang +2
Scaling test-time computation--generating and analyzing multiple or sequential outputs for a single input--has become a promising strategy for improving the reliability and quality…
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition
Wonjun Lee, Solee Im, Heejin Do +3
Dysarthric speech recognition often suffers from performance degradation due to the intrinsic diversity of dysarthric severity and extrinsic disparity from normal speech. To bridge…
Exploring Iterative Controllable Summarization with Large Language Models
Sangwon Ryu, Heejin Do, Daehee Kim +5
Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., leng…
An Investigation Into Explainable Audio Hate Speech Detection
Jinmyeong An, Wonjun Lee, Yejin Jeon +3
Research on hate speech has predominantly revolved around detection and interpretation from textual inputs, leaving verbal content largely unexplored. While there has been limited…
Key-Element-Informed sLLM Tuning for Document Summarization
Sangwon Ryu, Heejin Do, Yunsu Kim +2
Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial…
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
Sangwon Ryu, Heejin Do, Yunsu Kim +2
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a spec…