Search Results for author: Wojciech Kryściński

Found 18 papers, 12 papers with code

XGen-7B Technical Report

1 code implementation7 Sep 2023 Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong

Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.

2k 8k

LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond

1 code implementation23 May 2023 Philippe Laban, Wojciech Kryściński, Divyansh Agarwal, Alexander R. Fabbri, Caiming Xiong, Shafiq Joty, Chien-Sheng Wu

To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.


Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization

1 code implementation20 Dec 2022 Artidoro Pagnoni, Alexander R. Fabbri, Wojciech Kryściński, Chien-Sheng Wu

In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries.

Question Generation Question-Generation

Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors

1 code implementation25 May 2022 Liyan Tang, Tanya Goyal, Alexander R. Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryściński, Justin F. Rousseau, Greg Durrett

We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models.

Abstractive Text Summarization

Long Document Summarization with Top-down and Bottom-up Inference

no code implementations15 Mar 2022 Bo Pang, Erik Nijkamp, Wojciech Kryściński, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents.

Exploring Neural Models for Query-Focused Summarization

1 code implementation Findings (NAACL) 2022 Jesse Vig, Alexander R. Fabbri, Wojciech Kryściński, Chien-Sheng Wu, Wenhao Liu

Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.

Query-focused Summarization Transfer Learning

HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models

1 code implementation8 Oct 2021 Tanya Goyal, Nazneen Fatema Rajani, Wenhao Liu, Wojciech Kryściński

Summarization systems make numerous "decisions" about summary properties during inference, e. g. degree of copying, specificity and length of outputs, etc.

Abstractive Text Summarization Decoder +1

BookSum: A Collection of Datasets for Long-form Narrative Summarization

2 code implementations18 May 2021 Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev

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 stylistic biases.

Abstractive Text Summarization

SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

2 code implementations ACL 2021 Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema Rajani

Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.

Abstractive Text Summarization

FeTaQA: Free-form Table Question Answering

1 code implementation1 Apr 2021 Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev

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 include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.

Question Answering Retrieval +2

CTRLsum: Towards Generic Controllable Text Summarization

1 code implementation8 Dec 2020 Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong

Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.

Descriptive Reading Comprehension +1

What's New? Summarizing Contributions in Scientific Literature

no code implementations6 Nov 2020 Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong

To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles.


SummEval: Re-evaluating Summarization Evaluation

5 code implementations24 Jul 2020 Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev

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.

Text Summarization

Evaluating the Factual Consistency of Abstractive Text Summarization

4 code implementations EMNLP 2020 Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher

Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.

Abstractive Text Summarization Fact Checking +2

Neural Text Summarization: A Critical Evaluation

no code implementations IJCNLP 2019 Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher

Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document.

Diversity Text Summarization

Improving Abstraction in Text Summarization

no code implementations EMNLP 2018 Wojciech Kryściński, Romain Paulus, Caiming Xiong, Richard Socher

Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document.

Abstractive Text Summarization Decoder +2

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