Automatic text summarization is generally considered as a challenging task in the NLP community. One of the challenges is the publicly available and large dataset that is relatively rare and difficult to construct. The problem is even worse for low-resource languages such as Indonesian. In this paper, we present IndoSum, a new benchmark dataset for Indonesian text summarization. The dataset consists of news articles and manually constructed summaries. Notably, the dataset is almost 200x larger than the previous Indonesian summarization dataset of the same domain. We evaluated various extractive summarization approaches and obtained encouraging results which demonstrate the usefulness of the dataset and provide baselines for future research. The code and the dataset are available online under permissive licenses.
We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, information extraction, and NLP techniques and on-line resources. The system combines these features using a trainable feature combiner learned from summary examples through a machine learning algorithm. We demonstrate system scalability by reporting results on the best combination of summarization features for different document sources. We also present preliminary results from a task-based evaluation on summarization output usability. Text summarization is one of the solution for information overload. Reducing text without losing the meaning not only can save time to read, but also maintain the reader's understanding. One of many algorithms to summarize text is TextTeaser. Originally, this algorithm is intended to be used for text in English. However, due to TextTeaser algorithm does not consider the meaning of the text, we implement this algorithm for text in Indonesian language. This algorithm calculates four elements, such as title feature, sentence length, sentence position and keyword frequency. We utilize TextRank, an unsupervised and language independent text summarization algorithm, to evaluate the summarized text yielded by TextTeaser. The result shows that the TextTeaser algorithm needs more improvement to obtain better accuracy. In a scientific work, an abstract always contains main information of an article including at least a researched problem, aim(s), methodology, and result of the study. Writing an abstract requires a conscientious analysis since the contents would affect both the readers’ interestedness and disinterestedness on a particular or overall research topic. However, people generally write manually by summarizing the article. The aim of this study is constructing automation for summarizing Indonesian articles as an alternative approach to an abstract. This is involving two methods to summarize an article. A Term Frequency-inverse Document Frequency is used to get a keyword and weight terms, and a Vector Space Model is utilized to represent abstract text into a vector that used to identify the linkage of documents. From this method, the result of the summary can be generated from documents. Supporting this research, we used several journal articles written by a manual abstract. The results of this application show that the automatic summarization produces a paragraph which consists of more than three same sentences constantly as compared to manual paragraphing. The number of documents progressively increases especially for the electronic one. This degrades effectivity and efficiency in managing them. Therefore, it is a must to manage the documents. Automatic text summarization is able to solve by producing text document summaries. The goal of the research is to produce a tool to summarize documents in Bahasa: Indonesian Language. It is aimed to satisfy the user’s need of relevant and consistent summaries. The algorithm is based on sentence features scoring by using Latent Dirichlet Allocation and Genetic Algorithm for determining sentence feature weights. It is evaluated by calculating summarization speed, precision, recall, F-measure, and some subjective evaluations. Extractive summaries from the original text documents can represent important information from a single document in Bahasa with faster summarization speed compared to manual process. Best F-measure value is 0,556926 (with precision of 0.53448 and recall of 0.58134) and summary ratio of 30 . In this paper we present a system for generating summary by sentence extraction. To determine the weight of sentence, we use text features, such as sentence position, sentence relative length, average term frequency, keyword extraction, key phrase extraction, sentence similarity to the title, sentence centrality, inclusion of numerical data, inclusion of entity name, and inclusion of news emphasize words. We also investigate the effect of semantic feature, using latent semantic analysis, on the summarization task. Our experiments show that semantic feature increases precision and F-measure by 9.8 and 2.4 respectively in case of 20 Compression Rate. Key words: process, reduce, computer, method Text summary helps in understanding the content of a text without having to read the contents of the text as a whole. Automatic text summarization can be used to summarize the text easier. In this paper a frequent term based text summarization for Bahasa Indonesia is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The system counts nouns and verbs term frequency because they are considered as the most representative to the content of the text. The system also integrated to statistical approach with two underlying concepts such as title of the news article and location of the sentence. The generated summaries were compared with human generated summaries. Precision, recall and f-measure ratio are used to evaluate the accuracy of the generated summary. Assessment of the system summary result quality by respondents is also done by giving a value from 1 to 100. Based on the experimental results, the system is able to produce an effective summary with the average f-measure of 78 , at the compression rate of 30 . The average value of the quality of system summary result provided by respondents is 83,3