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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
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These services focus on creating ATS-friendly resumes, execute resume check and screening, and help with all of the preparation and application processes. Some of the AI builders, such as Leap.ai and Skillroads, concentrate on the resume creation while others, like Stella, also offer help with the job hunt itself as they match candidates to ...
An Applicant tracking system has 8 main use cases: Source qualified candidates, Job posting, Parse resumes, Filter and search candidate database, Rank and rate candidates, Manage and track applicants, Schedule applicant interviews, Communication tools like automated emails and reminders to candidates and hiring manager, Reporting and analytics ...
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
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