Automatic text summarization refers to the process of converting a lengthy text to a precise and fluent summary while preserving the overall meaning and highlighting the significant points of the original text. There are two approaches in text summarization as Extractive summarization Abstractive and summarization. Here we focus on implementing an Abstractive summarizer using Neural Networks.
When there is a high information overload, users often face difficulty in extracting relevant information from large text datasets. Doing this manually is not effective and it is time consuming. Other traditional methods of summarizing text are rule based and they fail to capture correct meanings, context, and the relationship between different parts of the text. Since there is diverse valuable textual data at present, there is a need for intelligent systems which are capable of generating accurate and coherent summaries automatically.
- E/20/148, Kasundie Hewawasam, e20148@eng.pdn.ac.lk
- E/20/316, Wethmi Ranasinghe, e20316@eng.pdn.ac.lk
- E/20/122, Rashmi Gunathilake, e20122@eng.pdn.ac.lk
- E/20/178, Dulanga Jayawardena, e20178@eng.pdn.ac.lk