50 citations · 122 across the 15 of their papers we have counts for
20 papers
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
Philippe Laban, Jesse Vig, Wojciech Kryscinski +3
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering co…
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
Philippe Laban, Wojciech Kryściński, Divyansh Agarwal +4
With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation…
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…
Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization
Artidoro Pagnoni, Alexander R. Fabbri, Wojciech Kryściński +1
In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries. In this paper, w…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski +2
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more pron…
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Liyan Tang, Tanya Goyal, Alexander R. Fabbri +6
The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of error…
Long Document Summarization with Top-down and Bottom-up Inference
Bo Pang, Erik Nijkamp, Wojciech Kryściński +3
Text summarization aims to condense long documents and retain key information. Critical to the success of a summarization model is the faithful inference of latent representations…
Exploring Neural Models for Query-Focused Summarization
Jesse Vig, Alexander R. Fabbri, Wojciech Kryściński +2
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released…
HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Tanya Goyal, Nazneen Fatema Rajani, Wenhao Liu +1
Summarization systems make numerous "decisions" about summary properties during inference, e.g. degree of copying, specificity and length of outputs, etc. However, these are implic…
BookSum: A Collection of Datasets for Long-form Narrative Summarization
Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal +2
The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and…
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
Jesse Vig, Wojciech Kryściński, Karan Goel +1
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.…
FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao +14
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to in…
CTRLsum: Towards Generic Controllable Text Summarization
Junxian He, Wojciech Kryściński, Bryan McCann +2
Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framew…
What's New? Summarizing Contributions in Scientific Literature
Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann +2
With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we intro…
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.…
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework
Michael Shum, Stephan Zheng, Wojciech Kryściński +2
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent. We propose Sketch-Fill-A-R, a framework that uses a persona-memory…
Evaluating the Factual Consistency of Abstractive Text Summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong +1
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, m…
Neural Text Summarization: A Critical Evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann +2
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the communit…
Improving Abstraction in Text Summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong +1
Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level…