Abstract:
Text summarization in the form of Headline
prediction for written articles becomes a popular
research recently. This paper presents a headline
prediction model using Recursive Recurrent Neural
Network (Recursive RNN) for Myanmar articles and
evaluates the performance by comparing with
sequence-to-sequence models. Recursive RNN model
use encoder-decoder LSTM model that generates a
single word forecast and call it recursively to text
summarization in the form of news headlines for
Myanmar news articles. A Recursive RNN, which
takes input as the sequence of variable length and has
to generate the variable length output by taking into
account the previous input. 5000 articles of Myanmar
news were collected to train headline prediction
model. The performance of Recursive RNN, Seq2Seq
with one-hot encoding and Seq2Seq with word
embedding (GloVe) were evaluated in terms of
ROUGE score values. The experimental results show
that Recursive RNN model significantly better than
other two models.
