Abstract:
Automatic text summarization is used as a
tool to help people in reducing the time spent
manually extracting the main ideas from text
documents. If the natural disaster news is
provided as the summary form including
important and relevant information, people in
management level can make comparisons and
intelligent decisions quickly without exhausting
energy by manually extracting the salient points.
Moreover, for a normal user, automatic
summary report of the disaster news makes them
clear perception and fully awareness of the
effects of the natural disaster by inspecting death
toll and damage of the natural hazards.
Therefore, this paper proposes Automatic
Myanmar Text Summarization framework that is
based on Information Extraction and practical
implementation of this framework in
summarizing natural disaster news which are in
seven types: Earthquake, Flood, Landslide,
Forest Fire, Tornado, Storm and Volcanic
Eruption described in Myanmar Language. The
two main components of the proposed
framework, Myanmar Word Segmentation model
based on Conditional Random Fields (CRFs)
and Information Extraction Model using CRFs approach are also introduced.
