31 citations · 45 across the 8 of their papers we have counts for
8 papers
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…
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
Yixin Liu, Alexander R. Fabbri, Pengfei Liu +8
Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation studies for summarization…
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…
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…