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Found 5 papers, 3 papers with code
End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks
The predominant approaches for extracting key information from documents resort to classifiers predicting the information type of each word.
Non-Parametric Memory Guidance for Multi-Document Summarization
Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents.
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets
In this work, we show that the number of parameters can be reduced further to $m^2/n$, in the best case, assuming the label set can be partitioned into $n$ conditionally independent subsets.
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data.
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning
We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC).
