1. A computer-implemented method comprising: learning sets of equivalent syntactic patterns from a corpus of documents; mapping the sets of equivalent syntactic patterns to corresponding items in a knowledge graph; receiving a set of one or more input documents; processing the set of one or more input documents for one or more expressions matching a first set of equivalent syntactic patterns from among the sets of equivalent syntactic patterns; processing the one or more expressions to determine one or more entities; determining a set of entities that are relevant to a main event described by the set of one or more input documents from the one or more entities; identifying entity types for the set of entities; generating a refined set of equivalent syntactic patterns by excluding the equivalent syntactic patterns with a relevance score below a predefined threshold; selecting an equivalent syntactic pattern from among the refined set of equivalent syntactic patterns for a headline, the selected equivalent syntactic pattern reflecting the main event described by the set of one or more input documents; generating the headline by populating the selected equivalent syntactic pattern with the one or more entities, wherein an order of the entities in the headline is based on the entity types of the one or more entities; determining one or more entries in the knowledge graph corresponding to the one or more entities described by the one or more expressions; and updating the one or more entries in the knowledge graph to reflect the main event using the headline.
3. The computer-implemented method of claim 1 , wherein learning the sets of equivalent syntactic patterns further includes: receiving sets of related documents; determining, for each of the sets of related documents, expressions involving corresponding information; determining sets of equivalent syntactic patterns based on the expressions; and storing the sets of equivalent syntactic patterns in a data store.