Abstract:
By providing information as the summary, the
reader can save time and can easily absorb the
main concepts of the articles which are
described in digital form. Therefore, automatic
text summarization, the process of compressing
the documents into the compact style by means of
a computer, plays an important role. In this
paper, Template-Driven Automatic Myanmar
Text Summarization using Conditional Random
Fields (CRFs) is introduced for Myanmar news
articles in natural disaster domain collected
from official Myanmar Newspaper. CRFs are
undirected graphical models which can be used
to segment and label natural language text. CRF
model is mainly used for information extraction
in this work.