Improving Sequence-to-Sequence Models for Abstractive Text Summarization
  Using Meta Heuristic Approaches
Aditya Saxena Ashutosh Ranjan
As human society transitions into the information age, reduction in our
attention span is a contingency, and people who spend time reading lengthy news
articles are decreasing rapidly and the need for succinct information is higher
than ever before. Therefore, it is essential to provide a quick overview of
important news by concisely summarizing the top news article and the most
intuitive headline. When humans try to make summaries, they extract the
essential information from the source and add useful phrases and grammatical
annotations from the original extract. Humans have a unique ability to create
abstractions. However, automatic summarization is a complicated problem to
solve. The use of sequence-to-sequence (seq2seq) models for neural abstractive
text summarization has been ascending as far as prevalence. Numerous innovative
strategies have been proposed to develop the current seq2seq models further,
permitting them to handle different issues like saliency, familiarity, and
human lucidness and create excellent synopses. In this article, we aimed toward
enhancing the present architectures and models for abstractive text
summarization. The modifications have been aimed at fine-tuning
hyper-parameters, attempting specific encoder-decoder combinations. We examined
many experiments on an extensively used CNN/DailyMail dataset to check the
effectiveness of various models.