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The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation net…
Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.
The Internet plays an increasingly important part in our daily lives as a source of written content for news and leisure. Yet it is tedious and difficult to sort through this staggering flow of information and stay updated with changes in our world, even using automated tools. Reading magazines and newspapers is too time-consuming, and there is …
This repository contains the implementation of a Transformer-based model for abstractive text summarization and a rule-based approach for extractive text summarization.
MOTS (MOdular Tool for Summarization) is a summarization system, written in Java. It is as modular as possible, and is intended to provide an architecture to implement and test new summarization methods, as well as to ease comparison with already implemented methods, in an unified framework.
MOTS (MOdular Tool for Summarization) is a summarization system, written in Java. It is as modular as possible, and is intended to provide an architecture to implement and test new summarization methods, as well as to ease comparison with already implemented methods, in an unified framework.