An Archetypal Analysis on

Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

Automatic text summarization: A comprehensive survey

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA). To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.

Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA.

Automatic generic document summarization based on non-negative matrix factorization ; Automatic generic document summarization based on non-negative matrix factorization

Sentiment summarization is the process of automatically creating a compressed version of the opinionated information expressed in a text. This paper presents a machine learning-based approach to summarize user's opinion expressed in reviews using: (1) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification. It integrates multiple strategies to tackle the following problems: sentiment shifter, the types of sentences and word coverage limit. (2) Word embedding model, a deep-learning-inspired method to understand meaning and semantic relationships among words and to extract a vector representation for each word. (3) Statistical and linguistic knowledge to determine salient sentences. The proposed method combines several types of features into a unified feature set to design a more accurate classification system (“True”: the extractive reference summary; “False”: otherwise). Thus, to achieve better performance scores, we carried out a performance study of four well-known feature selection techniques and seven of the most famous classifiers to select the most relevant set of features and find an efficient machine learning classifier, respectively. The proposed method is applied to three different datasets and the results show the integration of support vector machine-based classification method and Information Gain (IG) as a feature selection technique can significantly improve the performance and make the method comparable to other existing methods. Furthermore, our method that learns from this unified feature set can obtain better performance than one that learns from a feature subset.

Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment

Text Summarization is a way of determining the key concepts that are covered in the given text under consideration. Various techniques have been presented in literature and many are used in commercially available systems. The primary aim of the present work is to find efficacy of using sentiments for text summarization. In this work we present a computationally efficient technique based on sentiments of key words in text for the purpose of summarization. Sentiment analysis is already being used in various domains for analysis of large scale text data interpretation and opinion mining. The present work shows that sentiment analysis can also be used efficiently for the purpose of text summarization. We have tested our results on the standard DUC2002 datasets, and compared our results with different summarization approaches, viz. Random indexing based, LSA based, Graph based and Weighted graph based methods for different percentages of summarization. The proposed scheme is found to be efficient, in particular for 50% summarization.

Text Summarization Using Sentiment Analysis for DUC Data

Fuzzy Rough Sets are designed for decision-making with uncertainty, imprecision, and incompleteness in data. We propose to use Fuzzy Rough Sets for the task of sentence similarity-based Text Summar...

Fuzzy Rough Set-Based Sentence Similarity Measure and its Application to Text Summarization

Rough sets based span and its application to extractive text summarization

Information retrieval is widely used due to extremely large volume of text and image data available on the web and consequently, efficient retrieval is required. Text information retrieval is a branch of information retrieval which deals with text documents. Another key factor is the concern for a retrieval engine, often referred to as user-specific information retrieval, which works according to a specific user. This article performs a preliminary investigation of the proposed fuzzy rough sets-based model for user-specific text information retrieval. The model improves on the computational time required to compute the approximations compared to classical fuzzy rough set model by using Wikipedia as the information source. The technique also improves on the accuracy of clustering obtained for user specified classes.

Fuzzy Rough Set Based Technique for User Specific Information Retrieval: A Case Study on Wikipedia Data

Latent semantic analysis has been used successfully for extractive text summarization for years, while random indexing-based summarization has been recently proposed in the literature for text summarization. The random indexing-based summarization inherently uses graph-based ranking techniques. In this paper, we propose a hybrid technique of latent semantic analysis and random indexing for text summarization. Further, we have performed experiments to compare the results with several related baseline methods. The effectiveness of the hybrid method so developed is evident from the relative increase in the results over the baseline LSA-based technique.

Nidhika Yadav

Papers

Text Summarization Using Sentiment Analysis for DUC Data

Fuzzy Rough Set-Based Sentence Similarity Measure and its Application to Text Summarization

Rough sets based span and its application to extractive text summarization

Fuzzy Rough Set Based Technique for User Specific Information Retrieval: A Case Study on Wikipedia Data

Hybrid Latent Semantic Analysis and Random Indexing Model for Text Summarization