14. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: identifying a word weight for each word in a corpus based on a relevance of each word; receiving a document that comprises a plurality of sentences; assigning to each word in the plurality of sentences a tag indicative of a part of speech of the word; calculating a topic weight for each sentence based on the tags of the words in the sentence, wherein calculating the topic weight further comprises: calculating an event weight for the sentence based on the tags of the words in the sentence; calculating an entity weight for the sentence based on the tags of the words in the sentence; calculating a sentiment weight for the sentence as on the tags of the words in the sentence; and calculating the topic weight of the sentence as a weighted SUM of the event weight, the entity weight, and the sentiment weight of the sentence; calculating a sentence weight for each sentence based on a sum of the word weights of the words in the sentence, the topic weight for the sentence, and a position weight of the sentence within a paragraph; selecting the sentence with a highest sentence weight as a title for the document; and causing the title to be presented with the document to a user.
14. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: identifying a word weight for each word in a corpus based on a relevance of each word; receiving a document that comprises a plurality of sentences; assigning to each word in the plurality of sentences a tag indicative of a part of speech of the word; calculating a topic weight for each sentence based on the tags of the words in the sentence, wherein calculating the topic weight further comprises: calculating an event weight for the sentence based on the tags of the words in the sentence; calculating an entity weight for the sentence based on the tags of the words in the sentence; calculating a sentiment weight for the sentence as on the tags of the words in the sentence; and calculating the topic weight of the sentence as a weighted SUM of the event weight, the entity weight, and the sentiment weight of the sentence; calculating a sentence weight for each sentence based on a sum of the word weights of the words in the sentence, the topic weight for the sentence, and a position weight of the sentence within a paragraph; selecting the sentence with a highest sentence weight as a title for the document; and causing the title to be presented with the document to a user. 15. The machine-readable storage medium as recited in claim 14 , wherein calculating the event weight further comprises: extracting events from the sentence, wherein each event includes a verb or a verb and a noun; and calculating the event weight of the sentence as a sum of the word weights of the words in the extracted events.