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Extracting important sentences for public health surveillance information from Indonesian medical articles
Bhaskoro S.B.a, Akbar S.a, Supangkat S.H.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.The objective of this research is to summarize the text and maintain the core story (main idea) of medical articles to be used as public health surveillance information. This research extracts important sentences to keep the topic of the main idea only in a single medical article. The steps taken to obtain important sentences is to do a summary text that minimizes the number of paragraphs and minimize the sentence in the article, to perform feature selection comparison, parameter weighting and weighting values, and to classify sentence categories into topic sentences, supporting sentences and concluding sentences. Comparison to the test results performed includes feature selection consisting of title and noun, weighting consisting of TF-IDF and TF and parameter values maximal marginal relevance consisting of 0.4, 0.6, 0.7, 0.8 while classification of sentence categories in this study using multinomial naïve bayes method. The results obtained by performing a summary technique using feature selection title, weighting TF-IDF and MMR parameter value 0.7 to classify sentence categories get the results as follows 85.8% and 83.7% for calculation of precision and recall.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]important sentences,medical text,Multinomials,Sentence structures,Text summarization,weighting[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]feature selection,important sentences,medical text,naïve bayes multinomial,sentence structure,text summarization,weighting[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICTSS.2017.8288886[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]