Application of Different Algorithms to Optimize Abstractive Summarization

Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithms for abstractive summarization then on the basis of merits of various algorithms we discuss, how various algorithm may help to optimize Abstractive Summarization. Challenges in Abstractive summarization: Repetition or duplicity of the content Arrangement of words Different people have different meaning for the same content Number of times a content repeated Different people have different interest Nosiness in content Information diversity Methods for Abstractive summarization: 1. integer linear programming-based summarization framework [3] 2. Improved Semantic Graph Approach [4] 3. novel speech act-guided summarization approach [5] 4. Neural Abstractive Summarization with Diverse Decoding(NASDD) [6] 5. Supervised and unsupervised approach [7] 6. Neural network based representation [8] 7. Integer linear optimization [9] 8. Maximum L∞-norm and minimum entropy regularization [10] 9. Opizer-E and Opizer-A [11] 10. LSTM-CNN based ATSDL model [12] 11. novel concept-level approach [13]