Approach For Automatic Text Summarization Research Articles

A Hybrid Approach for Automatic Text Summarization by Handling Out-of-Vocabulary Words Using TextR-BLG Pointer Algorithm

A Hybrid Approach for Automatic Text Summarization by Handling Out-of-Vocabulary Words Using TextR-BLG Pointer Algorithm

A Review on Optimization-Based Automatic Text Summarization Approach

A Review on Optimization-Based Automatic Text Summarization Approach

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

AbstractDocument summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.

SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.

Introducing Word's Importance Level-Based Text Summarization Using Tree Structure

Text-summarization plays a significant role towards quick knowledge acquisition from any text-based knowledge resource. To enhance the text-summarization process, a new approach towards automatic text-summarization is presented in this article that facilitates level (word importance factor)-based automated text-summarization. An equivalent tree is produced from the directed-graph during the input text processing with WordNet. Detailed investigations further ensure that the execution time for proposed automatic text-summarization, is strictly following a linear relationship with reference to the varying volume of inputs. Further investigation towards the performance of proposed automatic text-summarization approach ensures its superiority over several other existing text-summarization approaches.

A New Biomimetic Method Based on the Power Saves of Social Bees for Automatic Summaries of Texts by Extraction

In this paper, the authors propose a new approach for automatic text summarization by extraction based on Saving Energy Function where the first step constitute to use two techniques of extraction: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the second step aims to optimize the results of the previous layer by the metaheuristic based on Bee Algorithm, the objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate, this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final layer aims to choose the best summary from the candidate summaries generated by bee optimization, the authors opted for the technique of voting with a simple majority.

Single Document Text Summarization Using Clustering Approach Implementing for News Article

Text summarization is an old challenge in text mining but in dire need of researcher's attention in the areas of computational intelligence, machine learning and natural language processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted. Traditional documents clustering algorithms use the full- text in the documents to generate feature vectors. Such methods often produce unsatisfactory results because there is much noisy information in documents. The varying-length problem of the documents is also a significant negative factor affecting the performance. This technique retrieves important sentence emphasize on high information richness in the sentence as well as high information retrieval. These multiple factors help to maximize coverage of each sentence by taking into account the sentence relatedness to all other document sentence. These related maximum sentence generated scores are clustered to generate the summary of the document. Thus we use k-mean clustering to these maximum sentences of the document and find the relation to extract clusters with most relevant sets in the document, these helps to find the summary of the document. The main purpose of k-mean clustering algorithm is to generate pre define length of summary having maximum informative sentences. In this paper we present the approach for automatic text summarization by extraction of sentences from the Reuters-21578 corpus which include newspaper articles and used clustering approach for extraction summary. Work done for Text Summarization is given in the section (II). Section (III) provided our methodology for Text Summarization, Section (IV) provide the result of our text summarization system.

Improvisation of the Document Summarization by Combining the IR Techniques with “Code-Quantity Memory and Attention” Linguistic Principles

In this paper we have given a new statistical approach for automatic text summarization by combing the Information Retrieval (IR) techniques with the linguistic principles code quantity, memory and attention to get the relevant sentences. In our approach we are distilling the most important information from the source to get the actual concept in the abridged form. We have initially removed the redundancy of the input document by using the Synonymous Cosine Similarity and we ranked the sentences based on the linguistic principles code quantity, memory and attention. Moreover, this method has been run over the test data, obtaining satisfactory results in the evaluation when compared with the MS Word Automatic Summarizer with respect to the human judgment.

A Statistical Approach to perform Web Based Summarization

Over the past decade more and more users of the Internet rely on the search engines to help them find the information they need. However the information they find depends to a large extent, on the ranking mechanism of the search engines they use. Not surprisingly it in general consists of a large amount of information that is completely irrelevant. Text summarization is a process of reducing the size of a text while preserving its information content. Text Summarization is an emerging technique for understanding the main purpose of any kind of documents. To visualize a large text document within a short duration and small area like PDA screen, summarization provides a greater flexibility and convenience. This research focuses on developing a statistical automatic text summarization approach, K-mixture probabilistic model, to enhancing the quality of summaries. Sentences are ranked and extracted based on their semantic relationships significance values. The objective of this research is thus to propose a statistical approach to text summarization. Keywords - Extraction, Keywords, Statistical approach, Text Summarization, Webpage. I. INTRODUCTION Finding out the information that users need from a large amount of data is a major problem of information retrieval .Search engine is certainly a useful tool for helping users of the Internet find the information they need quickly. Unfortunately, it, in general, consists of a great amount of information that is totally irrelevant. One of the problems is that useful information tends to spread over a large number of similar documents instead of being located in a single document, but it is extremely difficult to identify and retrieve them. Building a web document summarization system involves Researches in dependence analysis of webs document Clustering, automatic generating summarization and user interface. Most search engines use ranked lists to rank the importance of the return web pages in response to a user query so that the returned information is more relevant to whatever a user is looking for. However, the ranked lists are not summarized in term of topics and are not suitable for browsing task for a very simple reason. The returned information are not classified or categorized. In other words, the returned web pages are interleaved instead of appearing one after another in terms of its category. Thus, users need to waste a lot of time in filtering out all the irrelevant data— even if search engine providers put a lot of time and effort in developing more useful ranking mechanisms.