activity
20172022
most citedCyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing

169 citations · 385 across the 17 of their papers we have counts for

collaborators

27 papers

cs.CL2021

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

Yusen Zhang, Ansong Ni, Tao Yu +6

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…

cs.CL20211 cited

EmailSum: Abstractive Email Thread Summarization

Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao +1

Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the lo…

cs.CL2021

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

Yichen Jiang, Asli Celikyilmaz, Paul Smolensky +7

Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of informat…

cs.CL202127 cited

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Ming Zhong, Da Yin, Tao Yu +8

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may…

cs.CL2021

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley +4

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training…

cs.CV20201 cited

Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language

Hassan Akbari, Hamid Palangi, Jianwei Yang +6

Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi…