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Traceable Text: Deepening Reading of AI-Generated Summaries with
  Phrase-Level Provenance Links

Traceable Text: Deepening Reading of AI-Generated Summaries with Phrase-Level Provenance Links

19 September 2024
Hita Kambhamettu
Jamie Flores
Andrew Head
ArXiv (abs)PDFHTML

Papers citing "Traceable Text: Deepening Reading of AI-Generated Summaries with Phrase-Level Provenance Links"

3 / 3 papers shown
Title
Language Models are Few-Shot Learners
Language Models are Few-Shot Learners
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
...
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei
BDL
859
42,379
0
28 May 2020
TLDR: Extreme Summarization of Scientific Documents
TLDR: Extreme Summarization of Scientific Documents
Isabel Cachola
Kyle Lo
Arman Cohan
Daniel S. Weld
112
217
0
30 Apr 2020
A Discourse-Aware Attention Model for Abstractive Summarization of Long
  Documents
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan
Franck Dernoncourt
Doo Soon Kim
Trung Bui
Seokhwan Kim
W. Chang
Nazli Goharian
482
763
0
16 Apr 2018
1