P. Sharmila1,*, C. Deisy1, S. Parthasarathy2
Computer Systems Science and Engineering, Vol.45, No.1, pp. 377-393, 2023, DOI:10.32604/csse.2023.027481
Abstract With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty… More >
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