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Found 9 papers, 1 papers with code
Understanding in Artificial Intelligence
A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities.
Image-based table recognition: data, model, and evaluation
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.
Ranked #10 on
Table Recognition
on PubTabNet
Global Locality in Biomedical Relation and Event Extraction
Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining.
A Graph-theoretic Summary Evaluation for ROUGE
ROUGE is one of the first and most widely used evaluation metrics for text summarization.
A Unified Neural Network Model for Geolocating Twitter Users
Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation.
Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings
We present a new summary evaluation approach that does not require human model summaries.
A Semantically Motivated Approach to Compute ROUGE Scores
ROUGE is one of the first and most widely used evaluation metrics for text summarization.
Appraising UMLS Coverage for Summarizing Medical Evidence
When making clinical decisions, practitioners need to rely on the most relevant evidence available.
On Improving Informativity and Grammaticality for Multi-Sentence Compression
In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i. e., improving both informativity and grammaticality at the same time.
