Automatically modelling and distilling knowledge within AI. In other words, summarising the AI research firehose.
An android app to summarize any pdf uploaded to it
Uses a remote AI language model to prepare and post a summary of a Topic
The Python-based web app extracts and summarizes news using NewsAPI, newspaper3k, spacy, Pegasus and T5 from Hugging Face. It categorizes news articles and uses a graph-based summary feature to summarize multiple documents. The app works with news in any language supported by NewsAPI.
🤖 Joplin AI - Summarisation: The project aims to create note summaries to help users synthesize main ideas and arguments to identify salient points. This means that users will have a clear idea of what the note is about in a short piece of text with less mental effort.
SumItUp is a platform that allows users to generate summaries from various types of content, including audio, images, videos, GIFs, URLs, PDFs and books. It provides users with quick and concise summaries to help them understand the content more efficiently.
Using a deep learning model that takes advantage of LSTM and a custom Attention layer, we create an algorithm that is able to train on reviews and existent summaries to churn out and generate brand new summaries of its own.
Abstractive summarizers GPT2 vs XLNet vs BERT model
Material Science Predictor
Implementation of the paper Ranking Sentences for Extractive Summarisation using Reinforcement Learning
An online meeting summarizer with voice-based screenshots!
Summarize blog posts, news articles and read it on one go
Summarises the content of README files for any public GitHub repo
Extractive text summarisation tool developed for completion of my MSc ICT Software Implementation module. This is an overhaul of my original work.
Automated conversation summarisation using reinforcement learning. This project develops and evaluates RL-based models that generate concise, informative and less toxic summaries from a large variety of chat or dialogue data. Features include a modular summarisation pipeline, tuning on custom created dataset, and flexible evaluation metrics.
like homebrew but with less fizz. install binaries as fast and as easy as possible. no package manager, no recipes or metadata to update, just install and go bin, binaries, fast, installer
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