[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-190050-105":53,"doc-detail-190050-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","algorithm-1-hierarchical-trivia-miner","Algorithm 1 Hierarchical Trivia Miner","","Hierarchical Trivia Miner defines a top-down procedure to extract “good trivia” from article text by selecting surprising sections and ranking candidate trivia content using a similarity score. It computes similarity with TF-IDF-based keyword weighting (TopT FIDF or all-words variants) and optionally applies filtering based on whether an entity appears in a sentence. Experiments compare methods across categories and report precision/coverage trade-offs, including TF-IDF, IDF, and combinations with filtering, evaluated on multiple domains such as films, books, people, and locations.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/algorithm-1-hierarchical-trivia-miner/190050/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/algorithm-1-hierarchical-trivia-miner/190050.png","ImageObject",442,249,{"name":88,"@type":89},"Riley","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-30","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"How does the Hierarchical Trivia Miner choose candidate trivia content?","Question",{"text":108,"@type":109},"It builds candidates top-down by locating surprising contents from sections/subsections and then selecting the content with the highest TriviaScore based on similarity to a summary.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How is similarity between a summary and content computed?",{"text":113,"@type":109},"Similarity uses TF-IDF keyword weighting for the summary and either all words in the sentence or top TF-IDF words in the content, then combines them into a similarity score.",{"name":115,"@type":106,"acceptedAnswer":116},"What is the purpose of filtering in the algorithm?",{"text":117,"@type":109},"Filtering checks whether the specified entity appears in a sentence; sentences without the entity are treated as None, which changes the extracted trivia set and the scoring outcomes.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},190050,1789968116,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":79},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","| Algorithm 1 Hierarchical Trivia Miner |  |  |\n| --- | --- | --- |\n| 1:\u003Cbr>2:\u003Cbr>3:\u003Cbr>4:\u003Cbr>5:\u003Cbr>6:\u003Cbr>7:\u003Cbr>8:\u003Cbr>9:\u003Cbr>10:\u003Cbr>11:\u003Cbr>12:\u003Cbr>13:\u003Cbr>14:\u003Cbr>15:\u003Cbr>16:\u003Cbr>17:\u003Cbr>18:\u003Cbr>19:\u003Cbr>20:\u003Cbr>21:\u003Cbr>22: | function TOP-DOWN(ARTICLE, ENTITY) B Summary in ARTICLES Contents in ARTICLE N EntityName in ARTICLE TriSec SURPRISE (B; S) TriSen None\u003Cbr>while T riSen  None do\u003Cbr>if Subsec in TriSec then\u003Cbr>TriSubSec SURPRISE (B; TriSec) if Sub2 sec in TriSubSec then\u003Cbr>TriSub2 Sec\u003Cbr>SURPRISE (B; TriSubSec) if Sub3 sec in TriSub2 Sec then\u003Cbr>TriSub3 Sec\u003Cbr>SURPRISE (B; TriSub2 Sec)\u003Cbr>else\u003Cbr>TriP ara\u003Cbr>SURPRISE (B; TriSub2 Sec)\u003Cbr>else\u003Cbr>TriP ara\u003Cbr>SURPRISE (B; TriSubSec)\u003Cbr>else\u003Cbr>TriP ara SURPRISE (B; TriSec) TriSen SURPRISE (B; TriP ara) if ENTITY = True then\u003Cbr>TriSen FILTERING (T riSen; N) | return T riSen\u003Cbr>23: function SURPRISE(SUMMARY, CONTENTS)\u003Cbr>24: B Summary\u003Cbr>25: S Contents\u003Cbr>26: for Si in S do\u003Cbr>27: Sim SIMILARITY (B; Si)\u003Cbr>28: Surprise ~~ ~~S~~1~~im\u003Cbr>29: TriviaScore.Surprise\u003Cbr>30: TriContent arg max [TriviaScore]\u003Cbr>i\u003Cbr>return TriContent\u003Cbr>31: function SIMILARITY(SUMMARY, CONTENT)\u003Cbr>32: K 5\u003Cbr>33: T1 TopT FIDF (Summary; K)\u003Cbr>34: if CONTENT = TriP ara then\u003Cbr>35: T2 AllWordsInSentence\u003Cbr>36: else\u003Cbr>37: T2 TopT FIDF (TEXT; K)\u003Cbr>38: Similarity 􀀛 (T1 ; T2) return Similarity\u003Cbr>39: function FILTERING(SENTENCE, ENTITY)\u003Cbr>40: if Entity in Sentence then\u003Cbr>41: Trivia Sentence\u003Cbr>42: else\u003Cbr>43: Trivia None return Trivia |\n\n\n| Top 5 TF-IDF words |  | Top 5 IDF words |  |\n| --- | --- | --- | --- |\n| Summary\u003Cbr>(T1) | Surprising section\u003Cbr>(T2) | Summary\u003Cbr>(T1) | Surprising section\u003Cbr>(T2) |\n| cosmology Jane |  | achieved accused |  |\n| achieved Mason |  | breaking acid |  |\n| breaking disabilities |  | cosmologist action |  |\n| cosmologist drive |  | discusses additional |  |\n| discusses family |  | English afraid |  |\n\n\n| Method | Good Trivia | Trivia | Not trivia | NoMaj | Total | Pronoun | Cost |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Category | 5 | 34 | 46 | 15 | 100 | - | 12,956 |\n| TF-IDF | 25 | 46 | 13 | 16 | 100 | 8 | 117 |\n| TF-IDF\u003Cbr>+ Filtering | 25 | 50 | 9 | 16 | 100 | 2 | 469 |\n| IDF | 38y | 43y | 8 | 11 | 100 | 9 | 118 |\n| IDF\u003Cbr>+ Filtering | 28 | 48 | 8 | 16 | 100 | 4 | 506 |\n| Google | 12 | 2 | 1 | 2 | 17 | - | - |\n\n| Domain | Entity | Good Trivia |\n| --- | --- | --- |\n| Album | The Slim Shady LP | In the album's first week of release, The Slim Shady LP sold 283,000 copies, debuting at number two on the Billboard 200 chart behind TLC's FanMailand Britney Spears' debut ...Baby One More Time. |\n| Band | Muse (band) | Most earlier Muse songs lyrically dealt with introspective themes, including relationships, social alienation, and difficulties they had encountered while trying to establish themselves in their hometown. |\n| Book | The Lord of the Rings | The Lord of the Rings developed as a personal exploration by Tolkien of his interests in philology, religion (particularly Catholicism), fairy tales, Norse and general Germanic mythology, and also Celtic, Slavic, Persian, Greek, and Finnish mythology. |\n| City | San Francisco | Geographically, Oakland Airport is approximately the same distance from downtown San Francisco as SFO, but due to its location across San Francisco Bay, it is greater driving distance from San Francisco. |\n| Country | Indonesia | Indonesia has 8 UNESCO World Heritage Sites, including the Borobudur Temple Compounds and the Komodo National Park; and a further 19 in a tentative list that includes the Jakarta Old Town, Bunaken National Park, and Raja Ampat Islands. |\n| Film & TV | The Dark Knight Rises | On July 20, 2012, during a midnight showing of The Dark Knight Rises atthe Century 16 cinema in Aurora, Colorado, a gunman wearing a gas mask opened fire inside the theater, killing 12 people and injuring 58 others. |\n| People | Billie Eilish | She was raised vegetarian and regularly advocates for veganism on social media. |\n| Sports team | Inter Milan | The ca","cbCaitGwHCmtN1Ac","https://ap.wps.com/l/cbCaitGwHCmtN1Ac","pdf",534238,10,"English","# Algorithm 1 Hierarchical Trivia Miner\n## Top-down procedure and SURPRISE scoring\n## Similarity computation (TF-IDF/IDF) and filtering\n## Experimental results and domain examples","[{\"question\":\"How does the Hierarchical Trivia Miner choose candidate trivia content?\",\"answer\":\"It builds candidates top-down by locating surprising contents from sections/subsections and then selecting the content with the highest TriviaScore based on similarity to a summary.\"},{\"question\":\"How is similarity between a summary and content computed?\",\"answer\":\"Similarity uses TF-IDF keyword weighting for the summary and either all words in the sentence or top TF-IDF words in the content, then combines them into a similarity score.\"},{\"question\":\"What is the purpose of filtering in the algorithm?\",\"answer\":\"Filtering checks whether the specified entity appears in a sentence; sentences without the entity are treated as None, which changes the extracted trivia set and the scoring outcomes.\"}]","Algorithm 1 Hierarchical Trivia Miner | PDF",1788400552]