A Closer Look at Data Bias in Neural Extractive Summarization Models

A Closer Look at Data Bias in Neural Extractive Summarization Models

30 September 2019

Xipeng Qiu

Xuanjing Huang

Papers citing "A Closer Look at Data Bias in Neural Extractive Summarization Models"

9 / 9 papers shown

Title
SEMQA: Semi-Extractive Multi-Source Question Answering Tal Schuster Á. Lelkes Haitian Sun Jai Gupta Jonathan Berant W. Cohen Donald Metzler 28 13 0 08 Nov 2023
On the State of German (Abstractive) Text Summarization Dennis Aumiller Jing Fan Michael Gertz 11 1 0 17 Jan 2023
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain Dennis Aumiller Ashish Chouhan Michael Gertz ELM AILaw 24 35 0 24 Oct 2022
Unsupervised Multi-Granularity Summarization Ming Zhong Yang Liu Suyu Ge Yuning Mao Yizhu Jiao Xingxing Zhang Yichong Xu Chenguang Zhu Michael Zeng Jiawei Han 79 8 0 29 Jan 2022
BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models Yi Liu Guoan Zhang Puning Yu Jianlin Su Shengfeng Pan BDL SyDa 13 5 0 26 Sep 2021
How well do you know your summarization datasets? Priyam Tejaswin Dhruv Naik Peng Liu 10 26 0 21 Jun 2021
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems Yiran Chen Pengfei Liu Ming Zhong Zi-Yi Dou Danqing Wang Xipeng Qiu Xuanjing Huang ELM 12 24 0 11 Oct 2020
On Robustness and Bias Analysis of BERT-based Relation Extraction Luoqiu Li Xiang Chen Hongbin Ye Zhen Bi Shumin Deng Ningyu Zhang Huajun Chen 19 18 0 14 Sep 2020
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents Ramesh Nallapati Feifei Zhai Bowen Zhou 207 1,252 0 14 Nov 2016