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20192022
most citedQMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

27 citations · 63 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CL20227 cited

How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?

Hritik Bansal, Da Yin, Masoud Monajatipoor +1

Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models…

cs.CL20227 cited

Towards a Unified Multi-Dimensional Evaluator for Text Generation

Ming Zhong, Yang Liu, Da Yin +6

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimen…

cs.CL20221 cited

Things not Written in Text: Exploring Spatial Commonsense from Visual Signals

Xiao Liu, Da Yin, Yansong Feng +1

Spatial commonsense, the knowledge about spatial position and relationship between objects (like the relative size of a lion and a girl, and the position of a boy relative to a bic…

cs.CL2021

Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning

Da Yin, Liunian Harold Li, Ziniu Hu +2

Commonsense is defined as the knowledge that is shared by everyone. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they ar…

cs.CL20213 cited

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Xiao Liu, Da Yin, Yansong Feng +2

Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship…

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…