Automatic Summarization Task for News Article

Understanding the contents of numerous articles 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 produces 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 research proposed Extractive summarization for news article about Coruption in Indonesia. We use five classes of important word/ phrase and make them in one sentence as summary. We find that there are still opportunities to develop better outcomes that are better coherence and better accuracy Keywords: Abstractive, Extractive, Statistic, Natural Processing Language, News Article. 1. INTRODUCTION People are now depending on the Internet in searching for News Article. By using existing tools, such as Google, Yahoo, Bing and so forth, the documents are easy to obtain, in large quantities, and coming from various sources. To understand the content of the documents quickly, the readers should read the summary (or abstract) of the documents. For summary usually contain only the important sentences in short form, and represent the contents of the news article as a whole. The summary system was built by Luhn [1] in the late 1950s. He built abstracts of scientific articles, which are placed on the top position in the scientific paper format, so that the readers have a choice to understand the contents of a scientific article through the Abstract. The Abstract is written by Volume 6, No. 1, June 2018 EMITTER International Journal of Engineering Technology, ISSN: 2443-1168 23 the author manually, has few amount sentences and contains only important sentence from the paper. The problem occours when the amount of the articles is numerous. The readers obviously need more times to read and understand, despite of the summary system in each document. Therefore, the need of more advance summary system increases. An automated summary system is one if the advance. It is generated from a collection of important sentences from a paper, whereas important sentences are constructed from important word or phrase. The system starts from marking the important words with the degree of occurrence of words or phrase in entire of articles. Sentences are ranked based on the frequency of word occurrences. Top rank of the sentence list becomes summary sentence. Then the technique to choose important sentence evolve, the characteristic of sentence involves the position of the sentence, the length of the sentence and so on. These characteristics make the accuracy of summary increase. The common processes of summarization as shown in Fig. 1. However, this technique is low in coherence because the content of each sentence in the summary has no relation. This weakness triggers the researchers to develop new method like human-summaries. The main characteristic of human-summary sentences are having good relationship with each other, good sentence structure, and using rhetorical move from the beginning to the end of the summary. Researchers developed the abstractive concepts that resemble humansummary. This study looks at new opportunities to make summaries results more coherent. Several tasks will be discussed in Section 2, followed by the developed method in developing Summary in Section 3. Section 4 will discuss Experiment and Result. Finally, the last Section will explain the conclusion of this study and about the opportunity of the research development. Fig 1. The Process for Generating Summarization Automatically Volume 6, No. 1, June 2018 EMITTER International Journal of Engineering Technology, ISSN: 2443-1168 24 2. ABSTRACTIVE SUMMARIZATION Technology evolves along with needs. Similarly, the need for better summary results has led researchers to investigate the possibilities for automatic summary. Summaries made by humans have abstraction values, where sentences in summary are new sentences that essentially convey what is contained in the original article. The automatically generated abstractive summaries are expected to be better and closer to the human-built summary. Abstractive summary requires a deeper analysis of the text and the ability to generate new sentences, which provide an obvious advantage to improve the accuracy of a summary, reducing its redundancy and keeping a good compression rate. The tasks for generating abstractive summaries [2] are 1) sentence Compression that removes peripheral information from a sentence to shorten summary, 2) sentence Fusion, that merge information from multiple sentences and reduces redundancy in summary and 3) reorganization. Sentences to make the summary coherent. Most Abstractive summary task uses Natural Language Processing. The natural language is a task for parsing of sentences in the text. Parsing is identification the Parts of Speech of each word and the grammatical relations between the words in the sentence. The output of parsing is usually a tree of grammatical relations and dependencies between the words in the text. These trees are called dependency trees. 2.1 Sentence Compression Sentence compression can be broadly described as the task of creating a grammatical summary of a single sentence with minimal information loss. The task requires a quantity called inter-phrase dependency strength. In the training process, original sentences are parsed. The number of tokens is counted for each pair of phrases, and connected with each other by a dependency path of certain length. The statistics is used to estimate the interphrase dependency strength required in the sentence compression process. Prior tasks mostly use Decision Tree algorithm [3], random forest, and gradient boosting algorithms to analyze the data. Madanapali’s {1} research used Intersection Algorithm to align paired sentences and a swallow parser to combine the sentences. The sentence was mapped into the structure of the predicate, get news content of phrase, and compare the predicate. Phrases that contain general information were selected, sorted, and added with some entities. Those were combined and arranged to generate the Summary. The results could be concluded using a tree that can improve the quality of the language of the summary significantly, and simultaneously minimize repetition. Unfortunately this approach did not involve the context of text when exchange between sentences. Furthermore, Genest and G. Lapalme [2] proposed a short summary and a good abstract from several articles in the same topic. This scheme was the extraction of information based on heuristic content selection and one or Volume 6, No. 1, June 2018 EMITTER International Journal of Engineering Technology, ISSN: 2443-1168 25 more rules to generate sentences. Each abstraction is tailored to the topic or sub category. When the rule was raised, multiple verbs and nouns that have the same meaning were omitted and the position of the rule was identified. The extraction process found several candidate rules on each topic of each category. Based on information extraction module, the content module selected the best rule candidate from each category and passed it to the summary generation module. The best candidates of the content were selected using a single rule that extracted and adapted to one or two categories of rule, and then updated the rule to construct a summary sentence. This study had the potential to make a summary with more information. However, the method was purely using a rule written manually that takes a lot of time. Another research was conducted by Trevor C and Mirella L [4]. The task was simply deleting word and rewrite sentence using additional such as, recording, insertion. They developed grammar rule for given source to set of possible output. Applying a series of grammar rules created each rule. Where each rule match a fragment of the source and creates a fragment of the target tree. A rule in the grammar consisted of a pair of elementary trees and a mapping between the variables in both trees. A derivation was a sequence of rules yielding a target tree with no remaining variables. Each grammar rule could assign a weigh. These weigh are learnt in discriminative training, find to set of related sentences target for given source sentence and create output. 2.2 Sentence Fusion We cannot separate the task of compression and fusion when constructing a summary. Both of them have to be done in order to generate a summary. When a sentence has been compressed which generates two or more sentences that have the same intent, the fusion technique is required to combine the two sentences or more and then discard the repetitive words. The heuristic technique used is very closed to a number of rules because the most appropriate approach is to find a number of rules based on existing examples. Similarly, the fusion task, the rule is extracted using the language pattern, then is used to identify the piece of text. The Dependency trees are used in this task. They play a prominent role in sentence fusion. Sentences, which will be fused, are represented at first as dependency trees. These trees are merged in to a tree and converted to a sentence that is known as the fused sentence. Volume 6, No. 1, June 2018 EMITTER International Journal of Engineering Technology, ISSN: 2443-1168 26 Fig 2. The architecture of Gistexter [5] However, this study has not yielded satisfactory results. S. M. Harabagiu and F. Lacatusu, [5] built a summary of several news documents with the same