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.
no code implementations • 17 Jun 2019 • Sangwoo Cho, Hassan Foroosh
The words are then combined into a sentence to represent the video, as a sentence.
no code implementations • 17 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.
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.
no code implementations • 18 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.
Ranked #62 on Skeleton Based Action Recognition on NTU RGB+D
1 code implementation • EMNLP 2020 • Sangwoo Cho, Kaiqiang Song, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu
Amongst the best means to summarize is highlighting.
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.
1 code implementation • 24 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.
1 code implementation • 22 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.
Ranked #7 on Abstractive Text Summarization on CNN / Daily Mail
1 code implementation • 28 Oct 2022 • Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Fei Liu, Dong Yu
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #5 on Text Summarization on Pubmed
1 code implementation • 19 Dec 2022 • Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, Dong Yu
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
no code implementations • 24 May 2023 • Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Fei Liu
Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.
no code implementations • 8 Sep 2023 • Haopeng Zhang, Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Hongwei Wang, Jiawei Zhang, Dong Yu
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
3 code implementations • 15 Nov 2023 • Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.
no code implementations • 14 Dec 2023 • Kaiqiang Song, Xiaoyang Wang, Sangwoo Cho, Xiaoman Pan, Dong Yu
This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information.
1 code implementation • 7 Jan 2024 • Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, PengFei Liu, Dong Yu
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
no code implementations • 31 Jan 2024 • Sangwoo Cho, Kaiqiang Song, Chao Zhao, Xiaoyang Wang, Dong Yu
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.
no code implementations • 15 Feb 2024 • Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.
no code implementations • 6 Mar 2024 • Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.
1 code implementation • 2 Apr 2024 • Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Ruihong Huang, Dong Yu
To address this issue and make the summarizer express both sides of opinions, we introduce the concept of polarity calibration, which aims to align the polarity of output summary with that of input text.