  Abstractive summarization is the process of generating novel sentences based
on the information extracted from the original text document while retaining
the context. Due to abstractive summarization's underlying complexities, most
of the past research work has been done on the extractive summarization
approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq)
model, abstractive summarization becomes more viable. Although a significant
number of notable research has been done in the English language based on
abstractive summarization, only a couple of works have been done on Bengali
abstractive news summarization (BANS). In this article, we presented a seq2seq
based Long Short-Term Memory (LSTM) network model with attention at
encoder-decoder. Our proposed system deploys a local attention-based model that
produces a long sequence of words with lucid and human-like generated sentences
with noteworthy information of the original document. We also prepared a
dataset of more than 19k articles and corresponding human-written summaries
collected from bangla.bdnews24.com1 which is till now the most extensive
dataset for Bengali news document summarization and publicly published in
Kaggle2. We evaluated our model qualitatively and quantitatively and compared
it with other published results. It showed significant improvement in terms of
human evaluation scores with state-of-the-art approaches for BANS.
