Search Results for author:
Found 18 papers, 8 papers with code
How to Discern Important Urgent News?
We found that a simple property of clusters in a clustered dataset of news correlate strongly with importance and urgency of news (IUN) as assessed by LLM.
Linear Cross-Lingual Mapping of Sentence Embeddings
Semantics of a sentence is defined with much less ambiguity than semantics of a single word, and it should be better preserved by translation to another language.
Neural Embeddings for Text
We propose a new kind of embedding for natural language text that deeply represents semantic meaning.
BabyBear: Cheap inference triage for expensive language models
For named entity recognition, we save 33% of the deep learning compute while maintaining an F1 score higher than 95% on the CoNLL benchmark.
Named Entity Linking with Entity Representation by Multiple Embeddings
Our observations suggest that the minimal number of mentions required to create a knowledge base (KB) entity is very important for NEL performance.
Consistency and Coherence from Points of Contextual Similarity
Factual consistency is one of important summary evaluation dimensions, especially as summary generation becomes more fluent and coherent.
Namesakes: Ambiguously Named Entities from Wikipedia and News
We present Namesakes, a dataset of ambiguously named entities obtained from English-language Wikipedia and news articles.
Does Summary Evaluation Survive Translation to Other Languages?
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.
Towards Human-Free Automatic Quality Evaluation of German Summarization
Evaluating large summarization corpora using humans has proven to be expensive from both the organizational and the financial perspective.
Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings
The proposed ESTIME, Estimator of Summary-to-Text Inconsistency by Mismatched Embeddings, correlates with expert scores in summary-level SummEval dataset stronger than other common evaluation measures not only in Consistency but also in Fluency.
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation
The goal of a summary is to concisely state the most important information in a document.
Is human scoring the best criteria for summary evaluation?
A higher correlation with human scores is considered to be a fair indicator of a better measure.
Primer AI's Systems for Acronym Identification and Disambiguation
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 acronyms in text and disambiguate their meaning.
Sensitivity of BLANC to human-scored qualities of text summaries
We explore the sensitivity of a document summary quality estimator, BLANC, to human assessment of qualities for the same summaries.
Zero-shot topic generation
We present an approach to generating topics using a model trained only for document title generation, with zero examples of topics given during training.
Fill in the BLANC: Human-free quality estimation of document summaries
We present BLANC, a new approach to the automatic estimation of document summary quality.
Headline Generation: Learning from Decomposable Document Titles
We propose a novel method for generating titles for unstructured text documents.
