Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot's effecters during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot's motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximate world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.

Active mobile robot localization by entropy minimization

Text summarization is the process of extracting sal ient information from the source text and to presen t that information to the user in the form of summary. It is very difficult for human beings to manually summarize large documents of text . Automatic abstractive summarization provides the r equired solution but it is a challenging task because it requires de eper analysis of text. In this paper, a survey on a bstractive text summarization methods has been presented. Abstractive summarization methods are classified into t wo categories i.e. structured based approach and seman tic based approach. The main idea behind these methods has been discussed. Besides the main idea, the strengths and weaknesses of each method have al so been highlighted. Some open research issues in abst ractive summarization have been identified and will address for future research. Finally, it is conclud ed from the literature studies that most of the a bstractive summarization methods produces highly coherent, cohesive, information rich and less redundant summary.

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A review on abstractive summarization methods

It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

A Review on Automatic Text Summarization Approaches

Multi-document summarization (MDS) is an effective tool for information aggregation which generates an informative and concise summary from a cluster of topic-related documents. Our survey structurally overviews the recent deep learning based multi-document summarization models via a proposed taxonomy and it is the first of its kind. Particularly, we propose a novel mechanism to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences among various objective functions which are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting development of the field.

Multi-document Summarization via Deep Learning Techniques: A Survey.

The quantity of information on the internet is massively increasing and gigantic volume of data with numerous compositions accessible openly online become more widespread. It is challenging nowadays for a user to extract the information efficiently and smoothly. As one of the methods to tackle this challenge, text summarization process diminishes the redundant information and retrieves the useful and relevant information from a text document to form a compressed and shorter version which is easy to understand and timesaving while reflecting the main idea of the discussed topic within the document. The approaches of automatic text summarization earn a keen interest within the Text Mining and NLP (Natural Language Processing) communities because it is a laborious job to manually summarize a text document. Mainly there are two types of text summarization, namely extractive based and abstractive based. This paper focuses on the extractive based summarization using K-Means Clustering with TFIDF (Term Frequency-Inverse Document Frequency) for summarization. The paper also reflects the idea of true K and using that value of K divides the sentences of the input document to present the final summary. Furth more, we have combined the K-means, TF-IDF with the issue of K value and predict the resulting system summary which shows comparatively best results.

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Extractive based Text Summarization Using KMeans and TF-IDF

For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abundant. So, the automatic text summarization is demanded for salient information retrieval. Automatic text summarization is a system of summarizing text by computer where a text is given to the computer as input and the output is a shorter and less redundant form of the original text. An informative pr

http://www.issr-journals.org/links/papers.php?journal=ijias&application=pdf&article=IJIAS-13-070-05

Literature Review of Automatic Multiple Documents Text Summarization

In the time of overloaded online information, automatic text summarization is especially demanded for salient information retrieval from huge amount electronic text. For the blessing of World Wide Web, the mass of data is now enormous in its volume. Researchers realized this fact from various aspects and tried to generate an automatic abstract of the gigantic body of data from the commencement of the last half century. Numerous ways are there for characterizing different approaches to passage recapitulation: extractive and abstractive from single or compound document, objective of content abridgement, characteristic of text summarization, level of processing from superficial to profound and sort of article's content. A significant pr

http://www.issr-journals.org/links/papers.php?journal=ijias&application=pdf&article=IJIAS-13-144-05

Literature Review of Automatic Single Document Text Summarization Using NLP

A method has been proposed in this paper for Bengali news documents summarization which extracts significant sentences using the four major steps (a) preprocessing, (b) sentence ranking, (c) sentence clustering, and (d) summary generation. The noticeable feature of this method is the incorporation of the sentence frequency where redundancy elimination is a consequence. Another one remarkable aspect is sentence clustering on the basis of similarity ratio among sentences. The summary sentence selection is done from all the clusters so that there will be maximum coverage of information in summary even if information is found scattered in input document. Two sets of human generated summary have been utilized where one is to train the system and another is for performance evaluation. The proposed method has been found better while turning comparison with the latest state-of-the art method of Bengali news documents summarization. The results of performance evaluation show that the average Precision, Recall and F-measure values are 0.608, 0.664 and 0.632 respectively.

Automatic Bengali news documents summarization by introducing sentence frequency and clustering

An approach of automatic Bangla text summarization is presented here by enhancing an existing keyphrase-based method. The enhancement is accomplished with three steps as follows: (i) modifying the keyphrases selection process, (ii) including the first sentence in summary if it contains any title word and (iii) counting numerical figure which is presented in digits and words for sentence scoring. Step by step performance analysis of our proposed approach is portrayed for two datasets. Performance is measured with ROUGE (Recall Oriented Understudy for Gisting Evaluation) automatic evaluation package. The results, based on ROUGE-1 and ROUGE-2 scores, show that the proposed enhancement has significant influence for Bangla text summarization over existing keyphrase-based method.

Enhancement of keyphrase-based approach of automatic Bangla text summarization

This paper proposes an automatic method to summarize Bangla news document. In the proposed approach, pronoun replacement is accomplished for the first time to minimize the dangling pronoun from summary. After replacing pronoun, sentences are ranked using term frequency, sentence frequency, numerical figures and title words. If two sentences have at least 60% cosine similarity, the frequency of the larger sentence is increased, and the smaller sentence is removed to eliminate redundancy. Moreover, the first sentence is included in summary always if it contains any title word. In Bangla text, numerical figures can be presented both in words and digits with a variety of forms. All these forms are identified to assess the importance of sentences. We have used the rule-based system in this approach with hidden Markov model and Markov chain model. To explore the rules, we have analyzed 3,000 Bangla news documents and studied some Bangla grammar books. A series of experiments are performed on 200 Bangla news documents and 600 summaries (3 summaries are for each document). The evaluation results demonstrate the effectiveness of the proposed technique over the four latest methods.

Md. Majharul Haque

Papers

Literature Review of Automatic Multiple Documents Text Summarization

Literature Review of Automatic Single Document Text Summarization Using NLP

Automatic Bengali news documents summarization by introducing sentence frequency and clustering

Enhancement of keyphrase-based approach of automatic Bangla text summarization

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking