2 code implementations • 16 May 2024 • Seongmin Park, Kyungho Kim, Jaejin Seo, Jihwa Lee
We present HyperSum, an extractive summarization framework that captures both the efficiency of traditional lexical summarization and the accuracy of contemporary neural approaches.
2 code implementations • 21 Aug 2023 • Seongmin Park, Jinkyu Seo, Jihwa Lee
We open-source HyperSeg to provide a strong baseline for unsupervised topic segmentation.
no code implementations • TU (COLING) 2022 • Seongmin Park, Dongchan Shin, Jihwa Lee
To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems.
1 code implementation • COLING 2022 • Seongmin Park, Jihwa Lee
With just an off-the-shelf textual entailment model, LIME outperforms recent baselines in weakly-supervised text classification and achieves state-of-the-art in 4 benchmarks.
1 code implementation • WIT (ACL) 2022 • Seongmin Park, Jihwa Lee
We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs.
1 code implementation • EMNLP (insights) 2021 • Seongmin Park, Jihwa Lee
Text variational autoencoders (VAEs) are notorious for posterior collapse, a phenomenon where the model's decoder learns to ignore signals from the encoder.
no code implementations • 4 Aug 2021 • Seongmin Park, Dongchan Shin, Sangyoun Paik, Subong Choi, Alena Kazakova, Jihwa Lee
Fine-tuning pretrained language models (LMs) is a popular approach to automatic speech recognition (ASR) error detection during post-processing.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1