Text summarization implementing abstractive and extractive methods

How often do we come across paragraphs which contain important information but are too long to read? Most people tend to overlook humungous paragraphs at the expense of losing out on crucial information. This leads to a gap in topics which otherwise connect other significant concepts to make a meaningful learning experience - something we term as knowledge void. This report aims to highlight the importance of summarization using the two different methods of summarization – abstractive and extractive. We shall discuss the methods in detail including the methodologies, architectures and algorithms involved. This includes the preprocessing of data, the introduction of word embeddings, the application of algorithms like TextRank, building sequence-to-sequence models using LSTMs, applying encoder-decoder architecture and other advanced NLP techniques. We shall also evaluate our work using appropriate evaluation metrics. There has been experimentation using different approaches like unidirectional LSTMs, bidirectional LSTMs, a variety of tokenizers, and incorporation of attention layer to obtain the model with optimal accuracy and consistency.