Automatic generation of a speech by processing raw claims to a set of arguments

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.

A system comprising:

• (i) at least one storage device having stored thereon instructions for:
• 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 identifying relations between pairs of claims of a set of 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 the possible set of the predefined number of arguments;
• and
• (ii) at least one hardware processor configured to execute said instructions.