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The exponential growth of social media text information presents a challenging issue in terms of retrieving valuable information efficiently. Utilizing deep learning models, we can automatically generate keywords that express core content and topics of social media text, thereby facilitating the retrieval of critical information. However, the performance of deep learning models is limited by the labeled text data in the social media domain. To address this problem, this paper presents a hierarchical keyword generation method for low-resource social media text. Specifically, the text segment is introduced as a hierarchical unit of social media text to construct a hierarchical model structure and design a text segment recovery task for self-supervised training of the model, which not only improves the ability of the model to extract features from social media text, but also reduces the dependence of the keyword generation model on the labeled data in the social media domain. Experimental results from publicly available social media datasets demonstrate that the proposed method can effectively improve the keyword generation performance even given limited social media labeled data. Further discussions demonstrate that the self-supervised training stage based on the text segment recovery task indeed benefits the model in adapting to the social media text domain. [ABSTRACT FROM AUTHOR]

How to automatically generate a pool of keywords used by potential consumers is a challenging issue for advertisers in sponsored search advertising (SSA). Such a keyword pool serves as the base for market research and determines the feasible space of consequent keyword related decisions. This article presents a novel method for keyword generation with Wikipedia as a corpus of the source text (WIKG). Starting with a few seed keywords, the WIKG supports flexible keywords generation by taking advantage of Wikipedia's rich link structure to construct a graph of entry articles in an iterative way. The termination condition is determined by a threshold reflecting the tradeoff between coverage of the generated keyword set and its relevance to seed keywords. Experimental results show that the WIKG outperforms three baselines derived from the extant literature, in terms of both coverage and relevance. [ABSTRACT FROM AUTHOR]

We investigated intra-individual monitoring and regulation in learning from text in sixth-grade students and their teachers. In Experiment 1, students provided judgments of learning (JOLs) for six texts in one of three cue-prompt conditions (after writing delayed keywords or summaries or without a cue prompt) and then selected texts for restudy. Teachers also judged their students' learning for each text, while seeing - if present - the keywords or summaries each student had written for each text, and also selected texts for restudy. Overall, monitoring accuracy was low (.10 for students, −.02 for teachers) and did not differ between cue-prompt conditions. Regulation, indexed by the correlation between JOLs and restudy selections, was significant (−.38 for students, −.60 for teachers), but was also not affected by cue-prompt condition. In Experiment 2, teachers judged students' comprehension of six texts without knowing the students' names, so that only the keywords and summaries, not prior impressions, could inform judgments. Again, monitoring accuracy was generally low (.06), but higher for keywords (.23) than for summaries (−.10). These results suggest that monitoring intra-individual differences in students' learning is challenging for teachers. Analyses of the diagnosticity and utilization of keywords suggest that these may contain insufficient cues for improving teacher judgments at this level of specificity. [ABSTRACT FROM AUTHOR]

With proliferation in the last twenty years of electronic resources such as online databases and Internet search engines, the ability to generate and use keywords has become an essential skill in the "task definition" stage of the research process. However, this skill is a difficult one to teach to undergraduates who may not have had much prior experience with brainstorming. An Internet Website named KwMap.net that generates a list of related keywords for a given search word is proposed as a useful classroom tool for demonstrating and giving students practice in this skill. [ABSTRACT FROM AUTHOR]