11. A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to: receive a topic under consideration (TUC), wherein the TUC is a debatable topic in a free text format; learn 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; provide the TUC as input to the claim function, wherein the claim function is configured to mine at least one content resource; apply 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; learn 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; provide the TUC as input to the classification function; apply 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 identify relations between pairs of claims of a set of extracted claims; automatically aggregate the claims of the set of extracted claims into a plurality of clusters based on the identified relations; automatically generate 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 score 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 generate a speech, wherein the speech comprises a top scoring possible set of the possible set of the predefined number of arguments.
11. A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to: receive a topic under consideration (TUC), wherein the TUC is a debatable topic in a free text format; learn 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; provide the TUC as input to the claim function, wherein the claim function is configured to mine at least one content resource; apply 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; learn 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; provide the TUC as input to the classification function; apply 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 identify relations between pairs of claims of a set of extracted claims; automatically aggregate the claims of the set of extracted claims into a plurality of clusters based on the identified relations; automatically generate 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 score 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 generate a speech, wherein the speech comprises a top scoring possible set of the possible set of the predefined number of arguments. 16. The computer program product of claim 11 , wherein the generating of the plurality of arguments comprises: assessing the quality of the claims in each of said plurality of clusters and assigning each of said claims with a claim quality score, and selecting of said at least one claim of said each argument from claims of said cluster based on their quality score.