There are two main approaches to summarizing text documents; they are:
- Extractive Methods.
- Abstractive Methods.
Extractive text summarization involves the selection of phrases and sentences from the source document to make up the new summary. Techniques involve ranking the relevance of phrases in order to choose only those most relevant to the meaning of the source.
Abstractive text summarization involves generating entirely new phrases and sentences to capture the meaning of the source document. This is a more challenging approach, but is also the approach ultimately used by humans. Classical methods operate by selecting and compressing content from the source document.
Detailed Latest Updates and Releases on Summarization
Frameworks, Libraries, Approaches and Tutorials:
- OpenNMT
- Pytorch implementation of Abstractive summarization methods on top of OpenNMT
- MASS from Microsoft
- Facebook AI Research Sequence-to-Sequence Toolkit written in Python
- Text summarization using seq2seq and encoder-decoder recurrent networks in Keras
- Multiple implementations for abstractive text summarization
- Text Summarization Based on TextRank in Python
Helpful Blogs
- Google colab eco system , and how to integrate it with your google drive , this blog can prove useful DeepLearning Free Ecosystem
- Overview on the different appraches used for abstractive text summarization
- How to represent text for our text summarization task
- What seq2seq and why do we use it in text summarization
- Multilayer Bidirectional Lstm/Gru for text summarization
- Beam Search & Attention for text summarization
- Build an Abstractive Text Summarizer in 94 Lines of Tensorflow
- Pointer generator for combination of Abstractive & Extractive methods for Text Summarization
- Teach seq2seq models to learn from their mistakes using deep curriculum learning