Leading Sentence News TextRank

Abstract

Application of automatic text summarization is a popular Natural Language Processing task and often used in extracting lengthy content to produce short summary. This is a tedious yet time-consuming task. This study focuses on Malay news articles with the aim to select representative sentences for Malay news headline generation. The dataset used in the experiment is a collection of multi-genre Malay news published between year of 2017 and 2019 from Bernama.com. In this study, a leading sentence approach is applied in the TextRank with TF-IDF and Word2Vec as language models to perform salient sentence extraction. In the experiment, the top-ranking sentences extracted are based on the 15%, 20%, 25% and 30% of the original news content. The extracted contents are evaluation against the original news headline using ROUGE evaluation matric. The model shows that the inclusion of first sentence and first two sentences from the news are able to achieve significant improvement. This leading sentence approach is able to achieve improvement of the F1 score from 1.36 to 7.98. Besides that, the experiment also proofs that the ROUGE scores decrease as the percentage of extraction increase. Thus, the proposed method is fast and resource efficient as compared to other state-of-the-art Natural Language approach.