Text summarization using seq2seq in Keras

In this notebook, we will build an abstractive based text summarizer using deep learning from the scratch in python using keras

language + text generation + summarization using Keras and Sumy

Abstractive Text Summarization with Transformer networks implemented (from scratch) using Keras and Tensorflow

A stacked LSTM based Network for Text Summarization Using Keras

Abstractive Text Summarization using Transformer model

a keras implement for seq2seq text summarization method

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Abstract Text Summarization using Deep Learning Tensorflow and Keras

Text Summarization of news articles in Neural Networks using Keras

This is a summarization of representative methods in text classification using by keras and scikit-learn.

Text summarization in Keras

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Utilizes the Encoder/Decoder RNN with Attention to attempt to summarize chunks of text. Written in Keras.

I have created an abstractive based text summarizer using deep learning from the scratch in python using keras

This project involves creating an abstractive text summarizer using fine-tuning of the BART model. Leveraging Keras for model creation and deploying with Flask, the summarizer generates concise and coherent summaries from extensive texts. The model achieved 76,84% of accuracy and 0.1756 of loss.

In this project, used the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization.

In this project, I have used the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization.

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. This Specialization is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them. Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. If you would like to brush up on these skills, we recommend the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Text Summarization using Keras.

Text Summarization Using Keras

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Semester Project for our Machine Learning Course

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Simple Text Summarizer in Keras

Text summarizer using Python(keras)

Text Summarization of News Articles Using Keras RNN