Hierarchical Trivia Fact Extraction from W ikipedia Articles (2020)

Abstract: Recently, automatic trivia fact extraction has attracted much research interest. Modern search engines have begun to provide trivia facts as the information for entities because they can motivate more user engagement. In this paper, we propose a new unsupervised algorithm that automatically mines trivia facts for a given entity. Unlike previous studies, the proposed algorithm targets at a single Wikipedia article and leverages its hierarchical structure via top-down processing. Thus, the proposed algorithm offers two distinctive advantages: it does not incur high computation time, and it provides a domain-independent approach for extracting trivia facts. Experimental results demonstrate that the proposed algorithm is over 100 times faster than the existing method which considers Wikipedia categories. Human evaluation demonstrates that the proposed algorithm can mine better trivia facts regardless of the target entity domain and outperforms the existing methods.

## introduction modern search engines offer a rich knowledge panel that appears on the right side of a user screen as a search result for a given entity to improve user engagement; see an example in figure 1 . one piece of information presented in the knowledge panel can be a trivia fact about the given entity, which contributes to the effective user engagement @xcite . for example, the google search engine provides a trivia fact for a given entity to attract users' attention @xcite . successfully attracting users' attention can facilitate the users to revisit the search engine @xcite . in contrast, poor user engagement would result in the users' switching to a competing search engine @xcite . trivia facts are, therefore, intended to improve the search quality to increase user engagement by making the search process more interesting. in contrast to common, expected, or normal information, a trivia fact is an interesting fact that is unusual, unexpected, or unique @xcite . @xcite demonstrated that trivia facts are usually little-known facts, thus, well-known facts (e.g., basic background information) about a given entity cannot be considered as trivia. for example, taking confucius as an example, the following fact in the wikipedia article can be considered as trivia: "there is a grand-scale memorial ceremony, called seokjeon daeje, twice a year in south korea for confucius." it is unusual and unexpected for such a memorial ceremony to be held in south korea because confucius is a popular chinese philosopher and politician. in addition, surprising facts can be also considered as trivia because surprise and trivia worthiness are strongly correlated @xcite . for example, the sentence in the wikipedia article for the film 'the dark knight rises' states: "on july 20, 2012, during a midnight showing of the dark knight rises at the century 16 cinema in aurora, colorado, a gunman wearing a gas mask opened fire inside the theater, killing 12 people and injuring 58 others." such information is surprising and can be considered as trivia. those little-known and surprising facts from wikipedia articles can be used to attract users' attention when searching these entities. early efforts to mine trivia facts took advantage of relational databases for relational expressions using several functions such as relational algebra, in order to generate trivia questions @xcite . however, this approach requires human experts to generate a natural trivia question from obtained relational expressions. rather than using a relational database, @xcite used the notion of "rarity" to measure interestingness to discover interesting facts and hidden connections such as relationships between people. recently, @xcite demonstrated that a wikipedia article for a given entity can be a good source to mine trivia facts. in response to this report, there have been several attempts to develop automatic trivia fact extraction algorithms for various wikipedia article domains such as movies @xcite , tv shows @xcite , and people @xcite . in addition, dbpedia, which provides structured data for wikipedia contents, can be used to mine trivia facts for the domains of artists and actors @xcite . despite the success of the previous studies, however, the existing trivia fact mining algorithms face the following two issues: they are strongly dependent on the target domains and incur high computational cost. to address these issues, we aim to develop a new automatic trivia fact extraction algorithm that focuses on the wikipedia's hierarchical structure. figure 2 : the hierarchical structure of the confucius's wikipedia article. the structure in a wikipedia article has not been necessarily considered, even though each article shares a standard format for the hierarchical structure including its article summary. each wikipedia article begins with its summary, which allows users to capture information about the given entity at a glance. essentially, the summary presents the principal subjects by omitting less important content in the article. then, the wikipedia article is divided by content, following the summary. figure 2 shows an example hierarchical structure of the confucius's article. the text in his wikipedia article is hierarchically divided, starting from a section, a subsection, a paragraph to a sentence. while the second section "life" contains four subsections, the first section "name" does not contain subsections. therefore, in this paper, we exploit the wikipedia's shared format, the article summary and the hierarchical structure. we propose the "hierarchical trivia miner (htm)" algorithm, which extracts trivia facts in a top-down manner with a surprise score in a given article. the htm algorithm uses a single wikipedia article for a target entity and adopts a top-down approach; thus, it can mine trivia facts regardless of target domains and does not need high computation time because it gradually narrows down its search space for extracting trivia facts. human evaluation shows that our htm algorithm can extract trivia facts from wikipedia articles regardless of target domains. we also compare its performance of extracting trivia facts and the computation time with the previous method that makes use of wikipedia's category information. ## related work recently, significant progresses have been made for mining trivia facts, and wikipedia is currently widely used as a source to mine trivia facts. existing approaches can be divided into two types, extracting trivia sentences and generating trivia sentences. to extract trivia facts, @xcite employed the ranking support vector machine (svm). they utilized user-generated trivia from the internet movie database (imdb) as the training dataset and applied the trained model to a sentence in an entity's wikipedia article as a trivia candidate. thus, it selects trivia sentences from the target wikipedia article. however, the imdb dataset handles only the movie domain, and it would be very costly to create datasets for other domains. @xcite focused on the domains of hollywood actors and music artists to harvest trivia facts. they classified trivia facts from the dbpedia dataset using fusion-based convolutional neural networks by incorporating hand-crafted features of a given fact triple, such as an entity, a relation/predicate, and an object. trivia facts can also be generated using wikipedia articles. @xcite used the wikipedia's category information located at the bottom of articles. their unsupervised algorithm ranks each category name based on "surprise" and "cohesiveness" scores and generates trivia facts using a template, "a is a member of c", where "a" is a given entity and "c" is the selected category name. they used many articles from all categories of the target entity; thus, their algorithm incurs high computational cost. in addition, their algorithm only targets at the people domain because other domains do not necessarily contain trivia categories. @xcite focused on wikipedia's superlative tables as a natural source of interesting facts. the rows in the tables are sorted as the ranking of entities, based on the corresponding value such as the building's height. given the ranked list in the table, they tracked an interesting value and paired it with a candidate template to generate trivia facts. unlike previous methods, the proposed htm algorithm extracts trivia facts for a given entity, using a single wikipedia article based on its hierarchical structure and summary. using hierarchical information is currently widely applied in various tasks as a good indicator to improve the task performance, such as text classification @xcite ), text summarization @xcite , language modeling @xcite , and neural machine translation @xcite . to the best of our knowledge, the proposed htm algorithm represents the first approach that exploits the hierarchical information in a wikipedia article. ## hierarchical trivia miner in this section, we describe the proposed htm algorithm. algorithm 1 shows the architecture of the htm algorithm. ## conclusion and future work in this paper, we showed that wikipedia's hierarchical information can be applied for extracting trivia facts. we proposed hierarchical trivia miner, which is domain-independent and does not incur high computational cost. experimental results on eight domains demonstrated that the proposed htm algorithm can extract "trivia facts" regardless of the target domains because it considers a single wikipedia article for the target entity. in addition, the proposed algorithm employs top-down processing using hierarchical information, which can gradually narrow down its search space and reduce the total elapsed time to extract trivia facts. the current filtering method discards mined trivia facts from the htm algorithm if they contain no entity tokens. for the future work, we are planning to extend the filtering method by taking coreference resolution into account to address the problem of the current filtering method.