Systems and methods identify and provide interesting facts about an entity. An example method includes selecting documents associated with at least one unique fact trigger, the documents being from a document repository. The method also includes generating entity-sentence pairs from the documents and, for a first entity of the entities represented by the entity-sentence pairs, clustering the entity-sentence pairs for the first entity using salient terms occurring in the sentence. The method also includes determining a representative sentence for each of the clusters and providing at least one of the representative sentences in response to a query that identifies the first entity. Another example method includes determining that a query relates to an entity in a knowledge base, determining that the entity has an associated unique fact list, and providing at least one of the unique facts in the list in response to the query.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a data store storing unique fact triggers; a query handling system that includes: at least one processor, and a memory storing instructions that, when executed by the at least one processor, cause the query handling system to perform operations including: selecting, from a document repository, documents associated with at least one unique fact trigger, generating entity-sentence pairs for a first entity by: extracting sentences from the selected documents and, for each of at least some of the sentences: identifying a reference to a first entity in the sentence, and storing the sentence and an identifier for the document from which the sentence was extracted as an entity-sentence pair for the first entity, generating at least one main unique fact cluster for the first entity by clustering the entity-sentence pairs on salient terms, determining a representative sentence for each of the at least one main unique fact cluster; and providing at least one of the representative sentences in response to a query that identifies the first entity. 2. The system of claim 1, wherein selecting documents associated with at least one unique fact trigger includes, for a first document of the selected documents: identifying a unique fact trigger phrase in text associated with a link to the first document. 3. The system of claim 1, wherein selecting documents associated with at least one unique fact trigger includes, for a first document of the selected documents: identifying a unique fact trigger phrase in a first query stored in a data store of historical search records; and determining that the first document is identified in a search result for the first query in the data store of historical search records. 4. The system of claim 1, wherein selecting documents associated with at least one unique fact trigger includes, for a first document of the selected documents: eliminating from selection documents associated with at least one blacklist phrase, the blacklist phrase including at least one of lies, myths, and fake. 5. The system of claim 1, wherein the sentences selected from the documents are selected from a central portion that excludes comments. 6. The system of claim 1, wherein generating the at least one main unique fact cluster includes: determining a set of salient terms for each sentence, wherein the set of salient terms excludes stop words; and clustering a first sentence and a second sentence when the respective sets of salient terms are equivalent, where a first term and a second term are equivalent when: the first term and the second term are the same, the first term is a synonym of the second term, or the first term and the second term are numbers, regardless of a value represented by the numbers. 7. The system of claim 1, wherein providing at least one of the representative sentences in response to a query includes: providing all of the representative sentences as a scrollable list. 8. The system of claim 1, wherein for each entity-sentence pair stored, the first entity meets a topicality threshold, wherein the topicality threshold is a function of a type of the first entity and the topicality of the first entity to the document from which the sentence was extracted. 9. A method comprising: selecting, from a document repository, documents associated with at least one unique fact trigger; generating entity-sentence pairs from the documents selected, where an entity from a knowledge base is associated with a sentence in an entity-sentence pair when the sentence includes a mention of the entity; for a first entity of the entities represented by the entity-sentence pairs: clustering the entity-sentence pairs for the first entity using salient terms occurring in the sentence, the clustering resulting in main clusters, wherein at least one main cluster has a supporting cluster, and determining a representative sentence for each of the main clusters; and providing at least one of the representative sentences in response to a query that identifies the first entity. 10. The method of claim 9, further comprising, for each sentence of the entity-sentence pairs for the first entity: determining whether the sentence is classified as bad or good with respect to the first entity, wherein only sentences classified as good are eligible to be a representative sentence. 11. The method of claim 10, wherein determining whether the sentence is classified as bad or good includes: determining whether the sentence includes a pronoun; and responsive to determining that the sentence does include a pronoun: determining whether the pronoun refers to a topic of the sentence or to a noun in the sentence; and classifying the sentence as good when the pronoun refers to a topic of the sentence or to a noun in the sentence. 12. The method of claim 10, wherein determining whether the sentence is classified as bad or good includes: determining whether the sentence includes a noun referred to by a proper noun elsewhere in a source of the sentence; and responsive to determining that the sentence does include a noun referred to by a proper noun: determining that the noun refers to a topic of the sentence or that the sentence includes the proper noun, and classifying the sentence as good responsive to the determining. 13. The method of claim 9, wherein determining the representative sentence includes, for each main cluster: calculating a sentence score for each sentence in the entity-sentence pairs in the main cluster, the sentence score including an importance factor based on a semantic importance of the first entity to the sentence; and selecting a sentence with a highest sentence score as the representative sentence for the main cluster. 14. The method of claim 9, wherein determining the representative sentence includes, for each main cluster: calculating a sentence score for each sentence in the entity-sentence pairs in the main cluster, the sentence score including a document topicality score for the first entity to the document; and selecting a sentence with a highest sentence score as the representative sentence for the main cluster. 15. The method of claim 9, wherein determining the representative sentence includes, for each main cluster: calculating a sentence score for each sentence in the entity-sentence pairs in the main cluster, the sentence score being based on a document topicality score for the first entity, a semantic importance of the first entity to the sentence, a rank for a source of the sentence, and a length of the sentence; and selecting a sentence with a highest sentence score as the representative sentence for the main cluster. 16. A non-transitory medium having code segments stored thereon, the code segments, when executed by a processor cause the processor to: determine that a query relates to an entity in a knowledge base and includes a unique fact trigger; determine that the entity has an associated unique fact list, the unique fact list including sentences extracted from source documents that mention the entity; and provide the unique fact list in response to the query. 17. The non-transitory medium of claim 16, the code segments further causing the processor to: generate a list of documents from a document repository that are responsive to the query; and provide the documents that are responsive to the query with the unique fact list. 18. The non-transitory medium of claim 17, wherein the unique fact list is presented in a position of prominence with regard to the list of documents responsive to the query. 19. The non-transitory medium of claim 16, wherein the unique fact list is provided as a scrollable list. 20. A method comprising: determining that a query relates to an entity in a knowledge base; determining that the entity has an associated unique fact list, the unique fact list including sentences extracted from source documents that mention the entity; selecting at least one of the unique facts from the unique fact list; and providing the at least one unique fact in response to the query. 21. The method of claim 20, further comprising: ordering the unique facts in the unique fact list in a random order; and selecting, as the at least one unique fact, a first unique fact in the unique fact list. 22. The method of claim 20, further comprising: ordering the unique facts in the unique fact list in a random order; determining that the query includes a unique fact trigger; and providing, responsive to the determining, the unique facts in the random order in response to the query. 23. The method of claim 20, further comprising: obtaining, from the knowledge base, facts about the entity; and providing the facts from the knowledge base with the at least one unique fact, wherein the knowledge base lacks the unique fact about the entity.