Extraction Based Automatic Text Summarization System with HMM Tagger

Manne, Suneetha; Mohd., Zaheer Parvez Shaik; Fatima, S. Sameen

doi:10.1007/978-3-642-27443-5_48

Cited by 6 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The display of results is presented with the link to view the overview of content which presents the summarized text of the selected result without navigating to the page of the URL. The content summarization is performed by the summarization technique [18][19][20][21][22] of natural language processing [23][24][25]. In future, the scope of the NLP could be extended towards cloud based [26][27][28][29][30] processing of AI techniques.…”

Section: Architecturementioning

confidence: 99%

Design of optimal search engine using text summarization through artificial intelligence techniques

et al. 2020

View full text Add to dashboard Cite

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.

show abstract

Section: Architecturementioning

confidence: 99%

Design of optimal search engine using text summarization through artificial intelligence techniques

et al. 2020

View full text Add to dashboard Cite

show abstract

“…), which is indicative of its importance. This is commonly done by means of machine learning algorithms [19,32], statistical techniques [20,23], discoursed-based approaches [4,9], different types of linguistic knowledge, such as semantic [33], or by means of specialized resources, as for instance, Wikipedia [14]. In the end, the top score units up to a desired length are extracted.…”

Section: Text Summarizationmentioning

confidence: 99%

Application of Text Summarization techniques to the Geographical Information Retrieval task

Perea‐Ortega

Lloret

Ureña‐López

et al. 2013

Expert Systems with Applications

View full text Add to dashboard Cite

“…With this approach, redundancy is eliminated. Following [17], paper [10] also summarizes the document using the frequency of terms in the document. Another approach for reducing the redundancy was proposed in [5] named Optimal Co mbinatorial Covering Algorithm for Multi-document Su mmarization (OCCAMS) for scoring and extracting sentences.…”

Section: Previous Workmentioning

confidence: 99%

AMDS: Sentence Extraction Based Proficient Framework for Multi-Document Summarization

Balasubramanian¹,

Srinivasagan²,

Geetha³

2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

View full text Add to dashboard Cite

-Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS ) for efficient summarization of document, which achieves the aforementioned requirement.Here, the documents are preprocessed initially to remove the information that is less important. S ummary of each preprocessed document is obtained through the sentence extraction process. Single document summarization is carried out based on graph model. A ranking method named Ingenious Ranking (IR) is proposed to rank and order the extracted single document summaries.It ranks the sentences in the generated summaries of each document and incorporates the individual summaries to generate a concise summary.Empirical results presented in this paper demonstrate the efficiency of the proposed AMDS framework.

show abstract

Extraction Based Automatic Text Summarization System with HMM Tagger

Cited by 6 publications

References 6 publications

Design of optimal search engine using text summarization through artificial intelligence techniques

Design of optimal search engine using text summarization through artificial intelligence techniques

Application of Text Summarization techniques to the Geographical Information Retrieval task

AMDS: Sentence Extraction Based Proficient Framework for Multi-Document Summarization

Contact Info

Product

Resources

About