Learning by Semantic Similarity Makes Abstractive Summarization Better

18 February 2020

Papers citing "Learning by Semantic Similarity Makes Abstractive Summarization Better"

7 / 7 papers shown

Title
Controlling keywords and their positions in text generation Yuichi Sasazawa Terufumi Morishita Hiroaki Ozaki Osamu Imaichi Yasuhiro Sogawa 30 2 0 19 Apr 2023
InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation Pierre Colombo Chloe Clave Pablo Piantanida 40 41 0 02 Dec 2021
Automatic Text Evaluation through the Lens of Wasserstein Barycenters Pierre Colombo Guillaume Staerman Chloé Clavel Pablo Piantanida 27 41 0 27 Aug 2021
How well do you know your summarization datasets? Priyam Tejaswin Dhruv Naik Peng Liu 33 26 0 21 Jun 2021
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions Guillaume Staerman Pavlo Mozharovskyi Pierre Colombo Stéphan Clémenccon Florence dÁlché-Buc OOD 69 17 0 23 Mar 2021
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics Sebastian Gehrmann Tosin Adewumi Karmanya Aggarwal Pawan Sasanka Ammanamanchi Aremu Anuoluwapo ... Nishant Subramani Wei Xu Diyi Yang Akhila Yerukola Jiawei Zhou VLM 260 285 0 02 Feb 2021
Text Summarization with Pretrained Encoders Yang Liu Mirella Lapata MILM 261 1,436 0 22 Aug 2019