A Comparative Analysis on Hindi and English Extractive Text Summarization

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.

We have proposed an automatic speech summarization approach that extracts words from transcription results obtained by automatic speech recognition (ASR) systems. To numerically evaluate this approach, the automatic summarization results are compared with manual summarization generated by humans through word extraction. We have proposed three metrics, weighted word precision, word strings precision and summarization accuracy (SumACCY), based on a word network created by merging manual summarization results. In this paper, we propose a new metric for automatic summarization results, weighted summarization accuracy (WSumACCY). This accuracy is weighted by the posterior probability of the manual summaries in the network to give the reliability of each answer extracted from the network. We clarify the goal of each metric and use these metrics to provide automatic evaluation results of the summarized speech. To compare the performance of each evaluation metric, correlations between the evaluation results using these metrics and subjective evaluation by hand are measured. It is confirmed that WSumACCY is an effective and robust measure for automatic summarization.

Text summarization is the process of automatically creating a shorter version of one or more text documents. In this context, word-based, sentence-based and graph-based methods approaches are largely used. Among these, graph based methods for automatic text summarization produce summaries based on the relationships between sentences. These relationships may also support the creation of several text processing applications such as extractive and abstractive summaries, question-answering and information retrieval systems, among others. A new graph model for text processing applications is proposed in this paper. It relies on four dimensions (similarity, semantic similarity, co reference, discourse information) to create the graph. The rationale behind the proposal presented here is resorting to more dimensions than previous works, and taking into account co reference resolution, taking into account to the role of pronouns in connecting the sentences. Co reference was not used in any previous graph based summarization technique. An experiment was performed using the Text Rank algorithm with the presented approach, on the CNN corpus. The results show that the model proposed here outperforms the current approaches both quantitatively and qualitatively.

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

An efficient way to automatically classify documents may be provided by automatic text summarization, the task of creating a shorter text from one or several documents. This paper presents an assessment of the 15 most widely used methods for automatic text summarization from the text classification perspective. A naive Bayes classifier was used showing that some of the methods tested are better suited for such a task.

HNTSumm: Hybrid text summarization of transliterated news articles

Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

Increased availability of the Internet and social media has created another ‘world of data’ comprised of text, audio and video files. It is very difficult for a user to get the accurate summary or to comprehend the relevant and important items from the available media. Additionally, readers or evaluators of these data files are interested only in the relevant content or summary to be retrieved in the less duration from the source files. Automatic text summarization (ATS) is the only way to summarize single or multiple documents to obtain relevant content from the source files. Available ATS systems generate bad summaries and take a lot of time and space for long documents due to inaccurate encoding. Therefore, in this work, we have introduced an approach for extractive text summarization using sentence ranking. Experiments have been performed over BBC and CNN news datasets and evaluated in terms of ROUGE using N-gram Language Model. The quantitative values of the metrics show the effectiveness of the proposed approach for news datasets.

Text summarization is the process of creating a short, accurate and fluent summary of a longer text document. As plenty of digital data is available online, automatic text summarization methods greatly needed to help and understand the lengthy & complex documents quickly by discovering the relevant information. This paper proposes the text summarization method for short news articles and long scientific papers using unsupervised neural network model. The proposed method works in four main steps: input document pre-processing, feature extraction, feature enhancement and final summary generation. We have extracted combination of various statistical and linguistic features from input document, which helps in improving the quality of sentence selection. Further Restricted Boltzmann Machine (RBM) model is used to capture & enhance the discriminative, abstract features in an unsupervised way to improve the overall performance without losing any significant information. Sentences are scored based on enhanced feature set and top sentences are selected for final extractive summary. Performance of the proposed method is evaluated using Rouge score and compared with TextRank, LexRank, LSA & Luhn baseline methods and the results demonstrates that proposed methodology performs better compared to other methods.

Summarization of Text is extracting important information from a body of text and present it in the form of a concise summary. The need for summarization has increased in recent times. The importance of having a simple concise summary of information in the news, business and research domains is paramount. Automatic text summarization is a well known task in the NLP (Natural Language Processing) field. Text Summarization techniques can be large divided into two groups: Extractive summarization and Abstractive summarization. Extractive summarization is based on identifying key sentences or phrases from the source text and grouping them to produce a summary without rewriting or paraphrasing the original text. Abstractive summarization is based on utilizing a deeper understanding of the source text and generating new sentences, not present in the original text, which improves the summary by reducing redundancy and focusing on the meaning of the source text. In this study we implement and compare the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are. We aim to learn the pros and cons of the various techniques used by utilizing summary scoring as well as manual inspection of generated summary.

The exponential growth of text data on the World Wide Web as well as on databases off line created a critical need for efficient text summarizers that significantly reduce its size while maintaining its integrity. In this paper, we present a new multigraph-based text summarizer method. This method is unique in that it produces a multi-edge-irregular-graph that represents words occurrence in the sentences of the target text. This graph is then converted into a symmetric matrix from which we can produce the ranking of sentences and hence obtain the summarized text using a threshold. To test our method performance, we compared our results with those from the most popular publicly available text summarization software using a corpus of 1000 samples from 6 different applications: health, literature, politics, religion, science and sports. The simulation results show that the proposed method produced better or comparable summaries in all cases. The proposed method is fast and can be implement for real time summarization.

Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.

The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language’s morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.