Exploiting Semantic Term Relations in Text Summarization

The traditional frequency based approach to creating multi-document extractive summary ranks sentences based on scores computed by summing up TF*IDF weights of words contained in the sentences. In this approach, TF or term frequency is calculated based on how frequently a term (word) occurs in the input and TF calculated in this way does not take into account the semantic relations among terms. In this paper, we propose methods that exploits semantic term relations for improving sentence ranking and redundancy removal steps of a summarization system. Our proposed summarization system has been tested on DUC 2003 and DUC 2004 benchmark multi-document summarization datasets. The experimental results reveal that performance of our multi-document text summarizer is significantly improved when the distributional term similarity measure is used for finding semantic term relations. Our multi-document text summarizer also outperforms some well known summarization baselines to which it is compared.

Similitude Based Segment Graph Construction and Segment Ranking for Automatic Summarization of Text Document

With the increase in the amount of data and documents on the web, text summarization has become one of the significant fields which cannot be avoided in today’s digital era. Automatic text summarization provides a quick summary to the user based on the information presented in the text documents. This paper presents the automated single document summarization by constructing similitude graphs from the extracted text segments. On extracting the text segments, the feature values are computed for all the segments by comparing them with the title and the entire document and by computing segment significance using the information gain ratio. Based on the computed features, the similarity between the segments is evaluated to construct the graph in which the vertices are the segments and the edges specify the similarity between them. The segments are ranked for including them in the extractive summary by computing the graph score and the sentence segment score. The experimental analysis has been performed using ROUGE metrics and the results are analyzed for the proposed model. The proposed model has been compared with the various existing models using 4 different datasets in which the proposed model acquired top 2 positions with the average rank computed on various metrics such as precision, recall, F-score. HIGHLIGHTS Paper presents the automated single document summarization by constructing similitude graphs from the extracted text segments It utilizes information gain ratio, graph construction, graph score and the sentence segment score computation Results analysis has been performed using ROUGE metrics with 4 popular datasets in the document summarization domain The model acquired top 2 positions with the average rank computed on various metrics such as precision, recall, F-score GRAPHICAL ABSTRACT

Caviar-Sunflower Optimization Algorithm-Based Deep Learning Classifier for Multi-Document Summarization

This paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870.

A Systematic Survey on Multi-document Text Summarization

Automatic text summarization is a technique of generating short and accurate summary of a longer text document. Text summarization can be classified based on the number of input documents (single document and multi-document summarization) and based on the characteristics of the summary generated (extractive and abstractive summarization). Multi-document summarization is an automatic process of creating relevant, informative and concise summary from a cluster of related documents. This paper does a detailed survey on the existing literature on the various approaches for text summarization. Few of the most popular approaches such as graph based, cluster based and deep learning-based summarization techniques are discussed here along with the evaluation metrics, which can provide an insight to the future researchers.

Improving Legal Case Summarization Using Document-Specific Catchphrases

Legal case summarization is an important problem, and several domain-specific summarization algorithms have been applied for this task. These algorithms generally use domain-specific legal dictionaries to estimate the importance of sentences. However, none of the popular summarization algorithms use document-specific catchphrases, which provide a unique amalgamation of domain-specific and document-specific information. In this work, we assess the performance of two legal document summarization algorithms, when two different types of catchphrases are incorporated in the summarization process. Our experiments confirm that both the summarization algorithms show improvement across all performance metrics, with the incorporation of document-specific catchphrases.