SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Abstract

Automatic text summarization attempts to provide an effective solution to today's unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL and ESA methods are used to achieve sentence semantic parsing and to represent its words as a vector of weighted Wikipedia concepts, respectively. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. Results also tell that the system is scalable, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectoral semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.

Psychosocial Factor Identification in Cancer Patient Community

“…Besides, the analysis also highlights the challenge of accommodating standard NLP tools to Finnish language where more work is needed in order to match the performance of the tools in English text. Especially, the concept of textual argument and text summarization, in continuation of our work in [39][40], can be employed to ensure some inherent consistency and coherence to the textual outputs.…”

Unstructured Text Documents Summarization With Multi-Stage Clustering

“…The Page Rank (PR) value indicates the significance of a page. This value depends on the number and PR values of all pages that contain links to the current page [27], [43]. If each of pages B, C, and D have links only to page A, then PR (A) will be equal to the sum of PR (B), PR (C), and PR (D) since all links in this simple method will point to A:…”

“…Recently, the frequency-based weighting techniques are the preliminary researches in sentence extraction studies [9], [14], [31]. Similarly, the semantic assessment by latent analysis [19], [27], [39], Markov-models, and graph-oriented supervised and unsupervised techniques are under investigation [4], [16], [27]. In short, the extractive-summarization is a key area of research as per the current literature analysis [2], [18], [29], [37].…”

Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

“…That is, we first seek to identify the relations expressed in a sentence via SRL and only then dig for deeper semantics. Similar approaches are used in research oriented towards event extraction, especially for abstractive news events summarization [23,22,18]. Multiple IE approaches [12,9,7] use shallow semantic parsing as basis for obtaining deeper semantics within labeled predicate arguments.…”