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News Feature Scoring (NeFS) merupakan metode pembobotan kalimat yang sering digunakan untuk melakukan pembobotan kalimat pada peringkasan dokumen berdasarkan fitur berita. Beberapa fitur berita diantaranya seperti word frequency , sentence position , Term Frequency-Inverse Document Frequency (TF-IDF), dan kemiripan kalimat terhadap judul. Metode NeFS mampu memilih kalimat penting dengan menghitung frekuensi kata dan mengukur similaritas kata antara kalimat dengan judul. Akan tetapi pembobotan dengan metode NeFS tidak cukup, karena metode tersebut mengabaikan kata informatif yang terkandung dalam kalimat. Kata-kata informatif yang terkandung pada kalimat dapat mengindikasikan bahwa kalimat tersebut penting. Penelitian ini bertujuan untuk melakukan pembobotan kalimat pada peringkasan multi-dokumen berita dengan pendekatan fitur berita dan informasi gramatikal (NeFGIS). Informasi gramatikal yang dibawa oleh part of speech tagging (POS Tagging) dapat menunjukkan adanya konten informatif. Pembobotan kalimat dengan pendekatan fitur berita dan informasi gramatikal diharapkan mampu memilih kalimat representatif secara lebih baik dan mampu meningkatkan kualitas hasil ringkasan. Pada penelitian ini terdapat 4 tahapan yang dilakukan antara lain seleksi berita, text preprocessing , sentence scoring , dan penyusunan ringkasan. Untuk mengukur hasil ringkasan menggunakan metode evaluasi Recall-Oriented Understudy for Gisting Evaluation (ROUGE) dengan empat varian fungsi yaitu ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4. Hasil ringkasan menggunakan metode yang diusulkan (NeFGIS) dibandingkan dengan hasil ringkasan menggunakan metode pembobotan dengan pendekatan fitur berita dan trending issue (NeFTIS). Metode NeFGIS memberikan hasil yang lebih baik dengan peningkatan nilai untuk fungsi recall pada ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4 secara berturut-turut adalah 20,37%, 33,33%, 1,85%, 23,14%.  News Feature Scoring (NeFS) is a sentence weighting method that used to weight the sentences in document summarization based on news features. There are several news features including word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), and sentences resemblance to the title. The NeFS method is able to select important sentences by calculating the frequency of words and measuring the similarity of words between sentences and titles. However, NeFS weighting method is not enough, because the method ignores the informative word in the sentence. The informative words contained in the sentence can indicate that the sentence is important. This study aims to weight the sentence in news multi-document summarization with news feature and grammatical information approach (NeFGIS). Grammatical information carried by part of speech tagging (POS Tagging) can indicate the presence of informative content. Sentence weighting with news features and grammatical information approach is expected to be able to determine sentence representatives better and be able to improve the quality of the summary results. In this study, there are 4 stages that are carried out including news selection, text preprocessing, sentence scoring, and compilation of summaries. Recall-Oriented Understanding for Gisting Evaluation (ROUGE) is used to measure the summary results with four variants of function; ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4. Summary results using the proposed method (NeFGIS) are compared with summary results using sentence weighting methods with news feature and trending issue approach (NeFTIS). The NeFGIS method provides better results with increased value for recall functions in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 respectively 20.37%, 33.33%, 1.85%, 23.14%.

Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging