1. A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC), wherein the TUC is a debatable topic in a free text format; learning a claim function by applying a machine learning technique to a claim training dataset, wherein the claim training dataset comprises: a content item, and claims selected from the content item by a group of people; providing the TUC as input to the claim function, wherein the claim function is configured to mine at least one content resource; applying the claim function to the at least one content resource, to extract said set of claims, wherein each claim of said set of claims is a concise statement with respect to the TUC; learning a classification function by applying a machine learning technique to a classification training dataset, wherein the classification training dataset comprises: an example TUC, and claims that are classified with respect to the example TUC; providing the TUC as input to the classification function; applying the classification function to the extracted claims, to output one or more classification tags for each of the extracted claims, wherein the classification tags comprise at least one of: a tag indicating that a certain one of the extracted claims is a pro claim with respect to the TUC, a tag indicating that a certain one of the extracted claims is a con claim with respect to the TUC, a tag indicating that a certain one of the extracted claims is a factual claim, and a tag indicating that a certain one of the extracted claims is a moral claim; automatically identifying relations between pairs of claims of a set of the extracted claims; automatically aggregating the claims of the set of extracted claims into a plurality of clusters based on the identified relations; automatically generating a plurality of arguments from the plurality of clusters, wherein each of the arguments is generated from a cluster of the plurality of clusters, and wherein each of the arguments comprises at least one claim of the set of extracted claims; automatically scoring each possible set of a predefined number of arguments of the plurality of arguments, based on a quality of each argument of the predefined number of arguments and on diversity between the predefined number of arguments; and automatically generating a speech, wherein the speech comprises a top scoring possible set of said each possible set of the predefined number of arguments.