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Namesakes: Ambiguously Named Entities from Wikipedia and News
Oleg Vasilyev, Aysu Altun, Nidhi Vyas +3
We present Namesakes, a dataset of ambiguously named entities obtained from English-language Wikipedia and news articles. It consists of 58862 mentions of 4148 unique entities and…
Does Summary Evaluation Survive Translation to Other Languages?
Spencer Braun, Oleg Vasilyev, Neslihan Iskender +1
The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. If…
Towards Human-Free Automatic Quality Evaluation of German Summarization
Neslihan Iskender, Oleg Vasilyev, Tim Polzehl +2
Evaluating large summarization corpora using humans has proven to be expensive from both the organizational and the financial perspective. Therefore, many automatic evaluation metr…
Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings
Oleg Vasilyev, John Bohannon
We propose a new reference-free summary quality evaluation measure, with emphasis on the faithfulness. The measure is designed to find and count all possible minute inconsistencies…
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation
Nicholas Egan, Oleg Vasilyev, John Bohannon
The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics th…
Primer AI's Systems for Acronym Identification and Disambiguation
Nicholas Egan, John Bohannon
The prevalence of ambiguous acronyms make scientific documents harder to understand for humans and machines alike, presenting a need for models that can automatically identify acro…