Open AccessAutomatic Abstractive Summarization Task for New Article
Author(s) -
Afrida Helen
Publication year - 2018
Publication title -
emitter international journal of engineering technology
Language(s) - English
Resource type - Journals
eISSN - 2443-1168
pISSN - 2355-391X
DOI - 10.24003/emitter.v6i1.212
Subject(s) - automatic summarization , computer science , natural language processing , coherence (philosophical gambling strategy) , task (project management) , artificial intelligence , reading (process) , information retrieval , linguistics , statistics , philosophy , mathematics , management , economics
Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.