This article presents a novel automatic method (AutoSummENG) for the evaluation of summarization systems, based on comparing the character n-gram graphs representation of the extracted summaries and a number of model summaries. The presented approach is language neutral, due to its statistical nature, and appears to hold a level of evaluation performance that matches and even exceeds other contemporary evaluation methods. Within this study, we measure the effectiveness of different representation methods, namely, word and character n-gram graph and histogram, different n-gram neighborhood indication methods as well as different comparison methods between the supplied representations. A theory for the a priori determination of the methods' parameters along with supporting experiments concludes the study to provide a complete alternative to existing methods concerning the automatic summary system evaluation process.

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Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and Conclusions: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

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Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a claim and one or more premises justifying it. The proposed approach tries to identify segments that represent argument elements (claims and premises) on social Web texts (mainly news and blogs) in the Greek language, for a small set of thematic domains, including articles on politics, economics, culture, various social issues, and sports. The proposed approach exploits distributed representations of words, extracted from a large non-annotated corpus. Among the novel aspects of this work is the thematic domain itself which relates to social Web, in contrast to traditional research in the area, which concentrates mainly on law documents and scientific publications. The huge increase of social web communities, along with their user tendency to debate, makes the identification of arguments in these texts a necessity. In addition, a new manually annotated corpus has been constructed that can be used freely for research purposes. Evaluation results are quite promising, suggesting that distributed representations can contribute positively to the task of argument extraction.

Purpose -The purpose of this study is to develop a novel approach to e-participation, which is based on "passive crowdsourcing" by government agencies, exploiting the extensive political content continuously created in numerous Web 2.0 social media (e.g. political blogs and microblogs, news sharing sites and online forums) by citizens without government stimulation, to understand better their needs, issues, opinions, proposals and arguments concerning a particular domain of government activity or public policy. Design/methodology/approach -This approach is developed and elaborated through cooperation with potential users experienced in the design of public policies from three countries (Austria, Greece and the UK), using a combination of quantitative and qualitative techniques: co-operative development of application scenarios, questionnaire surveys, focus groups and workshops and, finally, in-depth interviews.Findings -A process model for the application of the proposed passive crowdsourcing approach has been developed, which is quite different from the one of the usual active crowdsourcing. Based on it, the functional architecture of the required supporting information and communication technologies (ICT) infrastructure has been formulated, and then its technological architecture has been designed, addressing the conflicting requirements: low response time and, at the same time, provision of sufficiently "fresh" content for policymakers. Practical implications -Taking into account that traditionally government agencies monitor what the press writes about them, our research provides a basis for extending efficiently these activities in the new electronic media world (e.g. newspapers websites, blogs and microblogs, online forums, etc.) to understand better the needs, issues, opinions, arguments and proposals raised by the society with respect to important domains of government activity and public policies. Social implications -The proposed approach provides a new channel for the "voice" of the society to be directly communicated to the government so that the latter can design its policies and activities based on the social needs and realities and not on oversimplified models and stereotypes. Originality/value -Our paper proposes a novel approach to e-participation, which exploits the Web 2.0 social media -but in a quite different way from previous approaches -for conducting "passive crowdsourcing", and elaborates it: it develops an application process model for it and also an ICT infrastructure for supporting it, which are quite different from the ones of the existing "active crowdsourcing" approaches.