Search Results for author: Sangwoo Cho

Found 10 papers, 5 papers with code

Salience Allocation as Guidance for Abstractive Summarization

1 code implementation22 Oct 2022 Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu

Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.

Abstractive Text Summarization

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

1 code implementation24 Dec 2021 Honguk Woo, Hyunsung Lee, Sangwoo Cho

While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs.

Combinatorial Optimization Information Retrieval +2

StreamHover: Livestream Transcript Summarization and Annotation

1 code implementation EMNLP 2021 Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.

Extractive Summarization

Self-Attention Network for Skeleton-based Human Action Recognition

no code implementations18 Dec 2019 Sangwoo Cho, Muhammad Hasan Maqbool, Fei Liu, Hassan Foroosh

In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.

Action Recognition Skeleton Based Action Recognition

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

no code implementations WS 2019 Sangwoo Cho, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.

Document Summarization Extractive Summarization +2

Spatio-Temporal Fusion Networks for Action Recognition

no code implementations17 Jun 2019 Sangwoo Cho, Hassan Foroosh

The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames.

Action Recognition

Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

1 code implementation ACL 2019 Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu

The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.

Document Summarization Multi-Document Summarization +1

Cannot find the paper you are looking for? You can Submit a new open access paper.