1 code implementation • 7 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.
1 code implementation • 23 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.
1 code implementation • 20 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.
no code implementations • COLING (CreativeSumm) 2022 • Divyansh Agarwal, Alexander R. Fabbri, Simeng Han, Wojciech Kryściński, Faisal Ladhak, Bryan Li, Kathleen McKeown, Dragomir Radev, Tianyi Zhang, Sam Wiseman
We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions.
1 code implementation • 25 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.
no code implementations • 15 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.
Ranked #1 on Text Summarization on Pubmed
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.
1 code implementation • 8 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.
2 code implementations • 18 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.
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.
1 code implementation • 1 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.
1 code implementation • 8 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.
no code implementations • 6 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.
5 code implementations • 24 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.
no code implementations • WS 2020 • Michael Shum, Stephan Zheng, Wojciech Kryściński, Caiming Xiong, Richard Socher
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent.
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.
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.
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.
Ranked #4 on Text Summarization on CNN / Daily Mail (Anonymized)