乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T06:35:59Z","timestamp":1725950159201},"reference-count":96,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2014,5,15]],"date-time":"2014-05-15T00:00:00Z","timestamp":1400112000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Asso for Info Science & Tech"],"published-print":{"date-parts":[[2015,4]]},"abstract":"In this article, we investigate the application of entity type models in extractive multi\u2010document summarization using automatic caption generation for images of geo\u2010located entities (e.g., W<\/jats:italic><\/jats:styled-content>estminster<\/jats:italic>\u2005A<\/jats:italic><\/jats:styled-content>bbey<\/jats:italic>) as an application scenario. Entity type models contain sets of patterns aiming to capture the ways geo\u2010located entities are described in natural language. They are automatically derived from texts about geo\u2010located entities of the same type (e.g., churches, lakes). We integrate entity type models into a multi\u2010document summarizer and use them to address the 2 major tasks in extractive multi\u2010document summarization: sentence scoring<\/jats:italic> and summary composition<\/jats:italic>. We experiment with 3 different representation methods for entity type models: signature words<\/jats:italic>, n\u2010gram language models,<\/jats:italic> and dependency patterns<\/jats:italic>. We evaluate the summarizer with integrated entity type models relative to (a) a summarizer using standard text\u2010related features commonly used in text summarization and (b) the W<\/jats:styled-content>ikipedia location descriptions. Our results show that entity type models significantly improve the quality of output summaries over that of summaries generated using standard summarization features and W<\/jats:styled-content>ikipedia summaries. The representation of entity type models using dependency patterns is superior to the representations using signature words and n\u2010gram language models.<\/jats:p>","DOI":"10.1002\/asi.23211","type":"journal-article","created":{"date-parts":[[2015,3,16]],"date-time":"2015-03-16T16:46:38Z","timestamp":1426524398000},"page":"721-738","source":"Crossref","is-referenced-by-count":3,"title":["Generating descriptive multi\u2010document summaries of geo\u2010located entities using entity type models"],"prefix":"10.1002","volume":"66","author":[{"given":"Ahmet","family":"Aker","sequence":"first","affiliation":[{"name":"Department of Computer Science University of Sheffield 211 Portobello S1 4DP Sheffield United Kingdom"}]},{"given":"Robert","family":"Gaizauskas","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Sheffield 211 Portobello S1 4DP Sheffield United Kingdom"}]}],"member":"311","published-online":{"date-parts":[[2014,5,15]]},"reference":[{"key":"e_1_2_13_2_1","unstructured":"Aker A. &Gaizauskas R.(2009 September).Summary generation for toponym\u2010referenced images using object type language models.Paper presented at the International Conference on Recent Advances in Natural Language Processing (RANLP) Borovets Bulgaria."},{"key":"e_1_2_13_3_1","volume-title":"Proceedings of the International Conference on Language Resources and Evaluation (LREC)","author":"Aker A.","year":"2010"},{"key":"e_1_2_13_4_1","doi-asserted-by":"crossref","unstructured":"Aker A. &Gaizauskas R.(2011).Understanding the types of information humans associate with geographic objects. 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