Text summarization
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 (seeAppendix A, Further topics in Reinforcement Learning). The performance was good on short input sequences and it deteriorated...