Search Results for author: Alexander R. Fabbri

Found 26 papers, 18 papers with code

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.


On Learning to Summarize with Large Language Models as References

1 code implementation23 May 2023 Yixin Liu, Alexander R. Fabbri, PengFei Liu, Dragomir Radev, Arman Cohan

Therefore, we investigate a new learning paradigm of text summarization models that considers the LLMs as the reference or the gold-standard oracle on commonly used summarization datasets such as the CNN/DailyMail dataset.

Contrastive Learning Text Summarization

Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization

no code implementations20 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

Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation

2 code implementations15 Dec 2022 Yixin Liu, Alexander R. Fabbri, PengFei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev

4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results.

Prompted Opinion Summarization with GPT-3.5

1 code implementation29 Nov 2022 Adithya Bhaskar, Alexander R. Fabbri, Greg Durrett

Large language models have shown impressive performance across a wide variety of tasks, including text summarization.

Text Summarization

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

1 code implementation11 Nov 2022 Alexander R. Fabbri, Prafulla Kumar Choubey, Jesse Vig, Chien-Sheng Wu, Caiming Xiong

We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed.

Informativeness Sentence Compression

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

2 code implementations25 May 2022 Liyan Tang, Tanya Goyal, Alexander R. Fabbri, Philippe Laban, Jiacheng Xu, Semih Yahvuz, Wojciech Kryściński, Justin F. Rousseau, Greg Durrett

The propensity of abstractive summarization systems to make factual errors has been the subject of significant study, including work on models to detect factual errors and annotation of errors in current systems' outputs.

Abstractive Text Summarization

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.

Transfer Learning

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

2 code implementations NAACL 2022 Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander R. Fabbri, Yejin Choi, Noah A. Smith

We therefore propose a generalization of leaderboards, bidimensional leaderboards (Billboards), that simultaneously tracks progress in language generation models and metrics for their evaluation.

Image Captioning Machine Translation +1

CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization

no code implementations14 Oct 2021 Prafulla Kumar Choubey, Alexander R. Fabbri, Jesse Vig, Chien-Sheng Wu, Wenhao Liu, Nazneen Fatema Rajani

Then, we fine-tune a base summarization model, which is trained on all training samples, on the clean (noisy) subset to obtain an \textit{expert} (\textit{anti-expert}) model.

Abstractive Text Summarization Informativeness

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

1 code implementation ACL 2021 Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev

While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.

Abstractive Text Summarization Argument Mining +2

Multi-Perspective Abstractive Answer Summarization

no code implementations17 Apr 2021 Alexander R. Fabbri, Xiaojian Wu, Srini Iyer, Mona Diab

A major obstacle for multi-perspective, abstractive answer summarization is the absence of a dataset to provide supervision for producing such summaries.

Community Question Answering

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

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

1 code implementation ACL 2020 Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.

Language Modelling Question Answering +2

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

1 code implementation4 Sep 2019 Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R. Fabbri, Irene Li, Dan Friedman, Dragomir R. Radev

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

Scientific Document Summarization

Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess

2 code implementations26 Jun 2019 Youngnam Lee, Youngduck Choi, Junghyun Cho, Alexander R. Fabbri, HyunBin Loh, Chanyou Hwang, Yongku Lee, Sang-Wook Kim, Dragomir Radev

Our model outperforms existing approaches over several metrics in predicting user response correctness, notably out-performing other methods on new users without large question-response histories.

Machine Translation TAG

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

no code implementations26 Nov 2018 Irene Li, Alexander R. Fabbri, Robert R. Tung, Dragomir R. Radev

The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation.

Sarcasm Analysis using Conversation Context

no code implementations CL 2018 Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan

To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn.

Sarcasm Detection

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