Text summarization is the process of automatically generating summarized text of the document test fed as an input by retaining the important information of the document. Text summarization condenses a big set of information in a concise manner; therefore, summaries play an important role in applications related to news/articles, text search, and report generation.
There are two types of summarization algorithms:
- Extractive summarization: Creates summaries by copying parts of the text from the input text
- Abstractive summarization: Generates new text by rephrasing the text or using new words that were not in the input text
The attention-based encoder decoder model created for machine translation (Bahdanau et al., 2014) is a sequence-to-sequence model and was able to generate abstractive summaries with good performance by achieving good ROUGE score (see Appendix...
