1 code implementation • dialdoc (ACL) 2022 • Xiachong Feng, Xiaocheng Feng, Bing Qin
Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently.
no code implementations • 1 Mar 2024 • Lei LI, Yuqi Wang, Runxin Xu, Peiyi Wang, Xiachong Feng, Lingpeng Kong, Qi Liu
To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.
no code implementations • 28 Dec 2023 • Liang Zhao, Xiaocheng Feng, Xiachong Feng, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu
In this survey, we present these advances towards length extrapolation in a unified notation from the perspective of PE.
no code implementations • 7 Aug 2023 • Xiachong Feng, Xiaocheng Feng, Xiyuan Du, Min-Yen Kan, Bing Qin
However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.
no code implementations • 2 May 2023 • Xiachong Feng, Xiaocheng Feng, Bing Qin
Generative agents that simulate human society show tremendous potential for further research and practical applications.
1 code implementation • 7 Apr 2023 • Kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu, Bing Qin
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
no code implementations • 23 Jan 2023 • Xiachong Feng, Xiaocheng Feng, Bing Qin
To mitigate this challenge, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively re-calibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thus reducing the discrepancy.
no code implementations • 7 Jul 2021 • Xiachong Feng, Xiaocheng Feng, Bing Qin
We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
1 code implementation • ACL 2021 • Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu
Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.
1 code implementation • 30 Apr 2021 • Yichong Huang, Xiachong Feng, Xiaocheng Feng, Bing Qin
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
1 code implementation • 7 Dec 2020 • Xiachong Feng, Xiaocheng Feng, Bing Qin, Xinwei Geng
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.
1 code implementation • CCL 2021 • Xiachong Feng, Xiaocheng Feng, Bing Qin, Ting Liu
In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.