44 citations · 157 across the 27 of their papers we have counts for
28 papers
Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding
Muhammed Yavuz Nuzumlalı, Alexander Fabbri, Irene Li +1
Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts…
MultiNRC: A Challenging and Native Multilingual Reasoning Evaluation Benchmark for LLMs
Alexander R. Fabbri, Diego Mares, Jorge Flores +5
Although recent Large Language Models (LLMs) have shown rapid improvement on reasoning benchmarks in English, the evaluation of such LLMs' multilingual reasoning capability across…
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Griffin Adams, Alexander Fabbri, Faisal Ladhak +2
Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard…
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Griffin Adams, Alexander R. Fabbri, Faisal Ladhak +2
Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, stand…
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…
On Learning to Summarize with Large Language Models as References
Yixin Liu, Kejian Shi, Katherine S He +5
Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarizat…
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Yixin Liu, Alexander R. Fabbri, Yilun Zhao +5
Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for refe…
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
Alexander R. Fabbri, Prafulla Kumar Choubey, Jesse Vig +2
State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summ…
Prompted Opinion Summarization with GPT-3.5
Adithya Bhaskar, Alexander R. Fabbri, Greg Durrett
Large language models have shown impressive performance across a wide variety of tasks, including text summarization. In this paper, we show that this strong performance extends to…
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…
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation
Irene Li, Thomas George, Alexander Fabbri +7
Effective human learning depends on a wide selection of educational materials that align with the learner's current understanding of the topic. While the Internet has revolutionize…
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
Swapnil Hingmire, Irene Li, Rena Kawamura +11
A classification scheme of a scientific subject gives an overview of its body of knowledge. It can also be used to facilitate access to research articles and other materials relate…
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Alexander R. Fabbri, Chien-Sheng Wu, Wenhao Liu +1
Factual consistency is an essential quality of text summarization models in practical settings. Existing work in evaluating this dimension can be broadly categorized into two lines…
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…
Surfer100: Generating Surveys From Web Resources, Wikipedia-style
Irene Li, Alexander Fabbri, Rina Kawamura +7
Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-intro…
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras +5
Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metric…
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
Alexander R. Fabbri, Xiaojian Wu, Srini Iyer +2
Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thr…
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
Xiangru Tang, Alexander Fabbri, Haoran Li +6
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information. Thus, compar…
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi +4
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.…
Multi-Perspective Abstractive Answer Summarization
Alexander R. Fabbri, Xiaojian Wu, Srini Iyer +1
Community Question Answering (CQA) forums such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of questions. Each question thread can receiv…
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Swapnil Hingmire +1
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prer…
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu, Rui Zhang, He Yang Er +21
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained f…
Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess
Youngnam Lee, Youngduck Choi, Junghyun Cho +6
Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are f…
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Alexander R. Fabbri, Irene Li, Tianwei She +2
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems hav…