No / Workplace email is quickly evolving to keep up with those who use it---and perhaps to make way for the next killer application. Has email redefined human communication and interaction? How have organizations and employees incorporated email into their processes? This article aims to answer these questions and start a discussion around issues of email in the workplace. We report the results of a quantitative survey on the role of email in organizations. This survey, which involved administering an email questionnaire to 600 employees of 50 U.K.-based organizations, found email to be extremely pervasive within organizations. It is considered a valuable medium of communication that sits comfortably amidst verbal and written media. The survey also demonstrated that attitudes toward and patterns of email usage are differentiated by gender, as well as by psychological issues such as confidence levels. Also, despite the increase in factors that might hamper the effectiveness and efficiency of email, such as spam and viruses, the survey findings suggest organizations have implemented an infrastructure to manage these issues so they have a limited impact on end users.

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Email has become a part of most people's lives, and the ever increasing amount of messages people receive can lead to email overload. We attempt to mitigate this problem using email thread summarization. Summaries can be used for things other than just replacing an incoming email message. They can be used in the business world as a form of corporate memory, or to allow a new team member an easy way to catch up on an ongoing conversation. Email threads are of particular interest to summarization because they contain much structural redundancy due to their conversational nature. Our email thread summarization approach uses machine learning to pick which sentences from the email thread to use in the summary. A machine learning summarizer must be trained using previously labeled data, i.e. manually created summaries. After being trained our summarization algorithm can generate summaries that on average contain over 70% of the same sentences as human annotators. We show that labeling some key features such as speech acts, meta sentences, and subjectivity can improve performance to over 80% weighted recall. To create such email summarization software, an email dataset is needed for training and evaluation. Since email communication is a private matter, it is hard to get access to real emails for research. Furthermore these emails must be annotated with human generated summaries as well. As these annotated datasets are rare, we have created one and made it publicly available. The BC3 corpus contains annotations for 40 email threads which include extractive summaries, abstractive summaries with links, and labeled speech acts, meta sentences, and subjective sentences. While previous research has shown that machine learning algorithms are a promising approach to email summarization, there has not been a study on the impact of the choice of algorithm. We explore new techniques in email thread summarization using several different kinds of regression, and the results show that the choice of classifier is very critical. We also present a novel feature set for email summarization and do analysis on two email corpora: the BC3 corpus and the Enron corpus.

Email has become a part of most people's lives, and the ever increasing amount of messages people receive can lead to email overload. We attempt to mitigate this problem using email thread summarization. Summaries can be used for things other than just replacing an incoming email message. They can be used in the business world as a form of corporate memory, or to allow a new team member an easy way to catch up on an ongoing conversation. Email threads are of particular interest to summarization because they contain much structural redundancy due to their conversational nature. Our email thread summarization approach uses machine learning to pick which sentences from the email thread to use in the summary. A machine learning summarizer must be trained using previously labeled data, i.e. manually created summaries. After being trained our summarization algorithm can generate summaries that on average contain over 70% of the same sentences as human annotators. We show that labeling some key features such as speech acts, meta sentences, and subjectivity can improve performance to over 80% weighted recall. To create such email summarization software, an email dataset is needed for training and evaluation. Since email communication is a private matter, it is hard to get access to real emails for research. Furthermore these emails must be annotated with human generated summaries as well. As these annotated datasets are rare, we have created one and made it publicly available. The BC3 corpus contains annotations for 40 email threads which include extractive summaries, abstractive summaries with links, and labeled speech acts, meta sentences, and subjective sentences. While previous research has shown that machine learning algorithms are a promising approach to email summarization, there has not been a study on the impact of the choice of algorithm. We explore new techniques in email thread summarization using several different kinds of regression, and the results show that the choice of classifier is very critical. We also present a novel feature set for email summarization and do analysis on two email corpora: the BC3 corpus and the Enron corpus.

Zugl.: Clausthal, Techn. Univ., Diss., 2007

Email has become a part of most people's lives, and the ever increasing amount of messages people receive can lead to email overload. We attempt to mitigate this problem using email thread summarization. Summaries can be used for things other than just replacing an incoming email message. They can be used in the business world as a form of corporate memory, or to allow a new team member an easy way to catch up on an ongoing conversation. Email threads are of particular interest to summarization because they contain much structural redundancy due to their conversational nature. Our email thread summarization approach uses machine learning to pick which sentences from the email thread to use in the summary. A machine learning summarizer must be trained using previously labeled data, i.e. manually created summaries. After being trained our summarization algorithm can generate summaries that on average contain over 70% of the same sentences as human annotators. We show that labeling some key features such as speech acts, meta sentences, and subjectivity can improve performance to over 80% weighted recall. To create such email summarization software, an email dataset is needed for training and evaluation. Since email communication is a private matter, it is hard to get access to real emails for research. Furthermore these emails must be annotated with human generated summaries as well. As these annotated datasets are rare, we have created one and made it publicly available. The BC3 corpus contains annotations for 40 email threads which include extractive summaries, abstractive summaries with links, and labeled speech acts, meta sentences, and subjective sentences. While previous research has shown that machine learning algorithms are a promising approach to email summarization, there has not been a study on the impact of the choice of algorithm. We explore new techniques in email thread summarization using several different kinds of regression, and the results show that the choice of classifier is very critical. We also present a novel feature set for email summarization and do analysis on two email corpora: the BC3 corpus and the Enron corpus. / Science, Faculty of / Computer Science, Department of / Graduate

The main point of this work is to describe complex solution of web-hosting server based on free software. This should provide main services such a post or www server. The work describes instalation of Unix(GNU/Linux) operating system. In addition are described individual services, evolved conrete implementations and also is spoken their installation and configuration. Though this text describe implementation of concrete application, some part of thist text contains generally information about installation, configuration and securing of linux servers.

Traditional email encryption methods are difficult to set up, as they require senders to obtain a message recipient's public key before a secure communication can be sent. Easy Accessible Secure Email (EASEmail) addresses the key establishment and exchange issues of encrypted email by using a lightweight symmetric key server. Users can send a secure email without establishing or exchanging keys with the recipient in advance. With usability as its primary goal, EASEmail strives to bring usable secure email communication to the masses.

Email stress: what are its causes? how is it measured? can it be solved? The literature review revealed that, despite the term being well used and recognised, discussions surrounding the root cause of email stress had reached little consensus and the concept was not well understood. By its very nature, email stress theory had fallen victim to the academic debate between psychological vs. physiological interpretations of stress which, as a result of either choice, limited more progressive research. Likewise an array of email management strategies had been identified however, whilst some generated quick successes, they appeared to suffer longevity issues and were not maintained a few months after implementation in the workplace. The purpose of this research was to determine whether email communication causes employees psychological and physiological stress and investigate the impact of email management strategies in the workplace. A pragmatic philosophy placed the research problem as central and valued the differences between paradigms to promote a mixed-method approach to research. The decision to pair both case studies and action research methods ensured a framework for presenting results and an actionable solution was achieved. In direct response to the research aims an original email stress measuring methodology was devised that combined various data collection tools to measure and investigate email stress. This research design was applied and evaluated 'email free time' and email filing. Results of the study showed an increased stress response to occur during email use, i.e. caused employees' increased blood pressure, heart rate, cortisol and perceived stress, and a number of adverse effects such as managing staff via email, social detachment, blame and cover-your-back culture were identified. Findings revealed 'email free time' was not a desirable strategy to manage email stress and related stressors, whereas email filing was found more beneficial to workers well-being. Consolidation of the data gathered from the literature review and research findings were used to develop an initial conceptualisation of email stress in the form of two models, i.e. explanatory and action. A focus group was conducted to validate the proposed models and a further investigation at the ? was carried out to critique the use of an email training intervention. The results showed some improvements to employees' behaviour after the training, e.g. improved writing style, email checked on fewer occasions each day and fewer sufferers of email addiction. The initial models devised, alongside the latter findings, were synthesised to create a single integrative multidimensional model of email stress and management strategies. The model made an original contribution to knowledge in terms of theory, i.e. to conceptualise email stress, and practice, i.e. to offer practical solutions to the email worker.