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.
@cite_12 proposed some scoring methods and used them with TF-IDF to rank and summarize news articles. Another work @cite_6 used latent Dirichlet allocation coupled with genetic algorithm to produce summaries for online news articles. Simple methods like naive Bayes has also been used for Indonesian news summarization @cite_27 , although for English, naive Bayes has been used almost two decades earlier @cite_22 . A more recent work @cite_8 employed a summarization algorithm called TextTeaser with some predefined features for news articles as well. @cite_28 used TF-IDF to convert sentences into vectors, and their similarities are then computed against another vector obtained from some keywords. They used these similarity scores to extract important sentences as the summary. Unfortunately, all these work do not seem to be evaluated using ROUGE, despite being the standard metric for text summarization research.
@cite_22 @cite_8 @cite_28 @cite_6 @cite_27 @cite_12
2544460281 2311769156 2114535528 912777836
The development of online news media grew in number in Indonesia. One technique of news articles summarization is guided summarization where the summary should contain important aspect information. Guided summarization techniques have been developed in the Text Analysis Conference (TAC) 2011 and one of the best methods is SWING by Jun-ping, et al The purpose of this study is to adapt the methods of SWING system to Indonesian news articles as well as integrating with News Aggregator system. In this research, the experiments have purpose to determine the best features and system configuration when adapted to Indonesian news articles. ROUGE-2 and ROUGE-SU4 is used to evaluate the results of the summary where a summary of the system results compared to the human-made summaries. The best system configuration produces summary with evaluation of ROUGE-2 0,31 and ROUGE-SU4 0,22 which is very close to the human-made summaries with a value of ROUGE-2 0,32 and ROUGE-SU4 0.24. In addition, the update summarization component can be run by giving a summary of updates without repeating the information. Adaptation from SWING system to Indonesian news articles is employing features such as sentence length (SL), category relevance score (CRS), category KL-Divergence (CKLD), bigram DFS (BDFS), Top n NE corpus, Top n NE topic, quote sentence removal, and building SVR model for each news category. We present a framework for sentiment analysis on tweets related to news items. Given a set of tweets and news items, our framework classifies tweets as positive or negative and links them to the related news items. For the classification of tweets we use three of the most used machine learning methods, namely Naive Bayes, Complementary Naive Bayes, and Logistic Regression, and for linking tweets to news items, Natural Language Processing (NLP) techniques are used, including Zemberek NLP library for stemming and morphological analysis and then bag-of-words method for mapping. To test the framework, we collected 6000 tweets and labeled them manually to build a classifier for sentiment analysis. We considered tweets and news in Turkish language only in this work. Our results show that Naive Bayes performs well on classifying tweets in Turkish. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine the impact of configuration variations in five versions of Reuters on the observed performance of classifiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. For indirect compararions, kNN, LLSF and WORD were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performances except for a Naive Bayes approach, the other learning algorithms also performed relatively well. We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence and its constituents (e.g., phrases) in the parsing tree. This enables us to draw on word-level to sentence-level supervisions derived from reference summaries. In addition, recursive neural networks are used to automatically learn ranking features over the tree, with hand-crafted feature vectors of words as inputs. Hierarchical regressions are then conducted with learned features concatenating raw features. Ranking scores of sentences and words are utilized to effectively select informative and non-redundant sentences to generate summaries. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that R2N2 outperforms state-of-the-art extractive summarization approaches.