Personalized Text Summarization Based on Important Terms Identification

Móro, Róbert; ́, M ́ria Bielikov

doi:10.1109/dexa.2012.47

Cited by 26 publications

(13 citation statements)

References 12 publications

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“…The main source of identifying the reader's preference is through annotation of the documents. Moro and Bielikov' [7] has mentioned that the system performs well by considering the domain in which the text is being used based on the reader's choice. The personalized system may not perform well for all domains as there are words having different interpretation in different domains.…”

Section: Related Workmentioning

confidence: 99%

Graph Based Technique for Hindi Text Summarization

Kumar

Yadav

Sharma

2015

Advances in Intelligent Systems and Computing

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Automatic Summarization is the process of generating or extracting the important sentences from the given input document. Since there are many such systems for English language so this proposed system is mainly focused on the Hindi language. The basic idea of this summarization system is to identify the important sentences and also to extract them based on its relevance with other sentences. In case of summarization the sentences in the summarized document should be meaningful and relevant to each other, which are achieved using sentential semantic analysis. For finding the relation between each sentence and also to analyze for the importance, the Graph based approach is found to be more appropriate. Based on the frequency of words occurrence in the input document, the sentences are ranked and the ranks are used to identify the important sentences in the document. The relevance between each sentence in the document with other sentences is found using semantic similarity. There may be same information conveyed by two different sentences whose semantic similarity score is very high. Such kind of sentences has to be kept only once in the output. For which an analysis has been performed over various semantically similar sentences. Finally, the identified relevant sentences are merged using the rank and the semantic analysis of the sentences. These identified sentences are rearranged to provide a proper meaningful summarized text to avoid textual continuity in the output text. The system is found to perform well in terms of precision, recall and F-measure with various input documents.

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Section: Related Workmentioning

confidence: 99%

Graph Based Technique for Hindi Text Summarization

Kumar

Yadav

Sharma

2015

Advances in Intelligent Systems and Computing

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“…However, this research is based on the user's interest and is not intended to identify the user's method of summarizing a document. Moro and Bielikova (2012) proposed a personalized text summarization based on identification of important terms. They used comments added by readers as one of the sources of personalization.…”

Section: Related Workmentioning

confidence: 99%

Exploring actor–object relationships for query-focused multi-document summarization

Valizadeh

Brazdil

2014

Soft Comput

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Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor-object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multidocument summarization tasks.Keyword User-based summarization · Actor-object relationship · Multi-document summarization · Ensemble summarizing system · Training data construction Communicated by V. Loia.M. Valizadeh (B) · P. Brazdil

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“…Using this scroll bar, users were able to identify sections of the document which were most frequently read and edited and they are thus most interested in. In [8] various types of user activity tracks such as in-text highlights or comments were used to create personalized summarization of the document for individual users. They improved the quality of summarizations created from document content by identifying the most important sections of the document from the point of view of multiple users and from the point of view of individual summarisation users.…”

Section: Introductionmentioning

confidence: 99%

User's Interest Detection through Eye Tracking for Related Documents Retrieval

evcech

Bieliková

2014

2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization

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In multiple studies, various implicit and explicit feedback types were used to quantify document quality and to identify user's interest in document content on both document and sub-document level. In this study we use eye movement data retrieved while reading a document to identify sections, user is most interested in. We describe multiple patterns in eye movement of users reading documents that can be used to identify important document fragments. The identified patterns are used to determine the user's interest in text fragments and to compare effectiveness of extracted document fragments and user created in-text highlights in the task of related document retrieval. Identified eye movement patterns allowed us to extract more important document sections compared to manual annotations with greater precision and consistency. Both types of compared interest indicators achieved similar performance in related document retrieval with the single most important difference: gaze based important section identification do not require active participation of the reader.

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Personalized Text Summarization Based on Important Terms Identification

Cited by 26 publications

References 12 publications

Graph Based Technique for Hindi Text Summarization

Graph Based Technique for Hindi Text Summarization

Exploring actor–object relationships for query-focused multi-document summarization

User's Interest Detection through Eye Tracking for Related Documents Retrieval

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