Displaying 1 to 12 from 12 results

gensim - Topic Modelling for Humans

  •    Python

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

awesome-text-summarization - The guide to tackle with the Text Summarization


The guide to tackle with the Text Summarization. To take the appropriate action, we need latest information. But on the contrary, the amount of the information is more and more growing. There are many categories of information (economy, sports, health, technology...) and also there are many sources (news site, blog, SNS...).

text-summarization-experiment - Experiment on text summarization techniques and exploring Tensorflow

  •    Jupyter

This project is our first attempt to make use of Tensorflow and specifically the textsum model. This model enables us to create article summaries in an automated way, which is one of the areas that we are currently researching. In the field of automated text summarization, Deep learning is currently the most promising approach. The whole project has been developed on Tensorflow 1.0.1. In the following sections you will find further details and instructions on how to complete the steps that were outlined above.

pythonrouge - Python wrapper for evaluating summarization quality by ROUGE package

  •    Perl

This is the python wrapper to use ROUGE, summarization evaluation toolkit. In this implementation, you can evaluate various types of ROUGE metrics. You can evaluate your system summaries with reference summaries right now. It's not necessary to make an xml file as in the general ROUGE package. However, you can evaluate ROUGE scores in a standard way if you saved system summaries and reference summaries in specific directories. In the document summarization research, recall or F-measure of ROUGE metrics is used in most cases. So you can choose either recall or F-measure or both of these of ROUGE evaluation result for convenience.

Text_Summarization_with_Tensorflow - Implementation of a seq2seq model for summarization of textual data

  •    Jupyter

Implementation of a seq2seq model for summarization of textual data using the latest version of tensorflow. Demonstrated on amazon reviews, github issues and news articles. I tried the network on three different datasets.

text-summarization-tensorflow - Tensorflow seq2seq Implementation of Text Summarization.

  •    Python

Simple Tensorflow implementation of text summarization using seq2seq library. Encoder-Decoder model with attention mechanism.

ctrl-sum - Resources for the "CTRLsum: Towards Generic Controllable Text Summarization" paper

  •    Python

This repo includes instructions for using pretrained CTRLsum models as well as training new models. CTRLsum is a generic controllable summarization system to manipulate text summaries given control tokens in the form of keywords or prefix. CTRLsum is also able to achieve strong (e.g. state-of-the-art on CNN/Dailymail) summarization performance in an uncontrolled setting.

factCC - Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

  •    Python

Updated manually annotated data files - fixed filepaths in misaligned examples. Updated model checkpoint files - recomputed evaluation metrics for fixed examples.

AutoBrewML - With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates

  •    Jupyter

Traditional machine learning model development is resource-intensive, requiring significant domain/statistical knowledge and time to produce and compare dozens of models. With automated machine learning, the time it takes to get production-ready ML models with great ease and efficiency highly accelerates. However, the Automated Machine Learning does not yet provide much in terms of data preparation and feature engineering. The AutoBrewML framework tries to solve this problem at scale as well as simplifies the overall process for the user. It leverages the Azure Automated ML coupled with components like Data Profiler, Data Sampler, Data Cleanser, Anomaly Detector which ensures quality data as a critical pre-step for building the ML model. This is powered with Telemetry, DevOps and Power BI integration, thus providing the users with a one-stop shop solution to productionize any ML model. The framework aims at ‘Democratizing’ AI all the while maintaining the vision of ‘Responsible’ AI. As we think about the future of technology, it resides in the notion of intelligence. At Microsoft, we have an approach that’s both ambitious and broad, an approach that seeks to Democratize Machine Learning & Artificial Intelligence, to take it from the high walls of ivory towers and make it accessible for all.

synopsis - Automagical summarization for webpages and articles. 🔥

  •    TypeScript

Automagical AI-powered summarization for webpages and articles. The following examples all use HTTPie, a more intuitive version of curl.

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