31 citations · 107 across the 23 of their papers we have counts for
5 papers · 1 filter
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…