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  2. Applicant tracking system - Wikipedia

    en.wikipedia.org/wiki/Applicant_tracking_system

    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, Manange and track applicants, Schedule applicant interviews, Communication tools like automated emails and reminders to candidates and hiring manager, Reporting and analytics ...

  3. A CEO shares how she spots AI in job applications and how it ...

    www.aol.com/im-ceo-recruitment-company-heres...

    Overall, rather than use AI to complete job applications, I advise candidates to use AI to help identify the key skills and attributes that hiring managers will be looking for.

  4. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    An AI-powered engine streamlines the complexity of job hunting by assessing information on job skills, salaries, and user tendencies, matching job seekers to the most relevant positions. Machine intelligence calculates appropriate wages and highlights resume information for recruiters using NLP, which extracts relevant words and phrases from text.

  5. Artificial intelligence in hiring - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_in...

    Companies commonly screen through the use of questionnaires, coding tests, interviews, and resume analysis. Artificial Intelligence already plays a major role in the screening process. Resumes can be analyzed using AI for desirable characteristics, such as a certain amount of work experience or a relevant degree.

  6. Résumé - Wikipedia

    en.wikipedia.org/wiki/Résumé

    A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same organization or another.

  7. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    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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