activity
20172022
most citedTemplate-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

7 citations · 11 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL20224 cited

CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing

Divyansh Agarwal, Alexander R. Fabbri, Simeng Han +7

This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative…

cs.CL2020

Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation

Alexander R. Fabbri, Simeng Han, Haoyuan Li +5

Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typicall…

cs.CL2020

SummEval: Re-evaluating Summarization Evaluation

Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann +3

The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.…

cs.CL20207 cited

Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Alexander R. Fabbri, Patrick Ng, Zhiguo Wang +2

Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has bee…

cs.CL2019

ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks

Michihiro Yasunaga, Jungo Kasai, Rui Zhang +4

Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. Thi…

cs.CL2018

Sarcasm Analysis using Conversation Context

Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker's sarcastic intent is not always apparent without addit…