Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

📖 Using deep learning and scraping to analyze/summarize articles! Just drop in any URL!

Enhancers are small regions of DNA that bind to proteins, which enhance the tran-scription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement structure of chromatin allows the positions far apart in the sequence to have the op-portunity to contact each other. Therefore, identifying enhancers and their strength is a complex and challenging task. In this article, a new prediction method based on deep learning is proposed to identify enhancers and enhancer strength, called iEnhancer-DCLA. Firstly, we use word2vec to convert k-mers into number vectors to construct an input matrix. Secondly, we use convolutional neural network and bidi-rectional long short-term memory network to extract sequence features, and finally use the attention mechanism to extract relatively important features. In the task of predicting enhancers and their strengths, this method has improved to a certain ex-tent in most evaluation indexes. In summary, we believe that this method provides new ideas in the analysis of enhancers.

Automatic generation of news article summaries using deep learning

Part 2 of Fake News Detector, where ML models: Logistic Regression, SVM, Random Forest, XGBoost, GradientBoost and Deep Learning NN Models: RNN/LSTM/GRU, and RNN/BiDirectional LSTM,GRU layers. CUDA GPU is enabled for model speedup when applicable. Additional Transformer model is used to summary each article and the models' accuracy are compared.

All News is a web app. A news api is used to fetch news data from newsapi.org and categorised into different categories like business, health, science, etc. The newspaper library of python is then used to download the complete article of each news title and then NLP is applied to get the summary of the news. Then an LSTM deep learning model is used to get the positive or negative sentiment out of the summary of the news article. The sentiment is shown along with the news description and its summary along with the probabilty of the sentiment being positive. The model had a 95% accuracy on the training data and close to 90% accuracy on the validation data

The repository consists of top 30 summaries of the articles in deep learning, which I thought to be interesting and important.

Flutter-based wikipedia article summary frontend with deep learning for user preferences targeted at replacing doom scrolling

This is a summary of CoCoNet tool article which is an efficient deep learning tool for viral metagenome binning.

Given an article, using NLP to automatically output a summary. A project from "Applied Deep Learning", a course of NTU.

An abstractive news article summarization system built using a sequence-to-sequence deep learning model to automatically generate concise, coherent, and factually accurate summaries from long news articles.

Article Summarizer using AI is a Natural Language Processing (NLP) based application that automatically generates a short and meaningful summary from a long article or text document. The system uses machine learning or deep learning techniques to identify key sentences and extract the most important information.

This project involves implementation of data analysis and deep learning techniques to develop a model trained on 3 Lakh new article records by using Sequence-to-Sequence modelling technique. The model will generate a one paragraph summary from the provided news article.

The objective of this project was to create a deep learning model that would generate a summary of a news article as an output using seq2seq modelling. Using the DailyMail News dataset from Kaggle.

Flask API based Machine Learning service which takes the article as input and returns a summary of that articles as output. Implemented using LSTM based Deep RNN architecture and trained on 100k Amazon Food Reviews Dataset. The Model has an accuracy rate of around 97%.

The GPT-2 News Article Summarizer is an interactive web application designed to automatically generate concise summaries from long-form news articles using a custom fine-tuned GPT-2 model. The project bridges deep learning with usability by offering a simple UI for real-time summarization and user feedback collection.

Our model represents abstractive text summarization using LSTM (Long-Short term memory network) based deep learning as a sequence-to-sequence model noting that this is many-to-many problem. Textual data is learned by using word embedding on the news article dataset to pertain the original meaning of the article. This contains the sequence to sequence model for text summarization which has given around 84% accurate summary for given text.

Our model represents abstractive text summarization using LSTM (Long-Short term memory network) based deep learning as a sequence-to-sequence model noting that this is many-to-many problem. Textual data is learned by using word embedding on the news article dataset to pertain the original meaning of the article. This contains the sequence to sequence model for text summarization which has given around 84% accurate summary for given text.