Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Using citation-context to reduce topic drifting on pure citation-based recommendation

Recent works in the area of academic recommender systems have demonstrated the effectiveness of co-citation and citation closeness in related-document recommendations. However, documents recommended from such systems may drift away from the main theme of the query document. In this work, we investigate whether incorporating the textual information in close proximity to a citation as well as the citation position could reduce such drifting and further increase the performance of the recommender system. To investigate this, we run experiments with several recommendation methods on a newly created and now publicly available dataset containing 53 million unique citation-based records. We then conduct a user-based evaluation with domain-knowledgeable participants. Our results show that a new method based on the combination of Citation Proximity Analysis (CPA), topic modelling and word embeddings achieves more than 20% improvement in Normalised Discounted Cumulative Gain (nDCG) compared to CPA

Task-specific Word Identification from Short Texts Using a Convolutional Neural Network

Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries (e.g., WordNet), which are often unavailable for many applications such as social discrimination detection and fake review detection. However, we often have a set of labeled short texts where each short text has a task-related class label, e.g., discriminatory or non-discriminatory, specified by users or learned by classification algorithms. In this paper, we focus on identifying task-specific words and phrases from short texts by exploiting their class labels rather than using seed words or lexical dictionaries. We consider the task-specific word and phrase identification as feature learning. We train a convolutional neural network over a set of labeled texts and use score vectors to localize the task-specific words and phrases. Experimental results on sentiment word identification show that our approach significantly outperforms existing methods. We further conduct two case studies to show the effectiveness of our approach. One case study on a crawled tweets dataset demonstrates that our approach can successfully capture the discrimination-related words/phrases. The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa