SideNet: Neural Extractive Summarization with Side Information

Summary of News , Page ranking algorithm implementation

High Quality Extractive Text Summarisation using the Stanford Parser in Java

The pointer-generator network does a better job at copying words from the source text. Additionally it also is able to copy out-of-vocabulary words allowing the algorithm to handle unseen words even if the corpus has a smaller vocabulary.

Automatic Summary Generation and Evaluation for the German Language

Open domain extractive question answering

The project uses a single a document as input(.txt) and a compresion ratio as input and generate a files that picks up significant sentences in that order from the document. It uses natural language processing stanford POS tagger and an algoritm that on the basis of similarity and distinctiveness calculates rank of each sentence and based on the…

A quick and dirty Extractive Sentence Ranking implementation, originally concieved for use with chatbots