Abstract: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

Abstract: A system and a computer-implemented method for ranking tabular data entities by likelihood of comprising answers for (natural language) queries, based on multimodal descriptions of the tabular data entities, comprising separate representations, which represent different aspects of the tabular data entities. The ranking is based on joint representations, generated from the query representation and separate representations of the tabular data entities' aspects, using gated multimodal units. The computer-implemented method may be used for applications such as web searches, data aggregation, and research tasks.

Abstract: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

Abstract: Multimodal table encoding, including: Receiving an electronic document that contains a table. The table includes multiple rows, multiple columns, and a schema comprising column labels or row labels. The electronic document includes a description of the table which is located externally to the table. Next, operating separate machine learning encoders to separately encode the description, schema, each of the rows, and each of the columns of the table, respectively. The schema, the rows, and the columns are encoded together with end-of-column tokens and end-of-row tokens that mark an end of each column and row, respectively. Then, applying a machine learning gating mechanism to the encoded description, encoded schema, encoded rows, and encoded columns, to produce a fused encoding of the table, wherein the fused encoding is representative of both a structure of the table and a content of the table.

Abstract: An embodiment calculates a point of view (POV) value for a subportion of an item of media content, including evaluating user inputs related to an automatically detected POV of the subportion of the item. The embodiment also calculates a non-factual cost for the subportion of the item indicative of an amount of the subportion of the item that lacks support in a fact-based corpora. The embodiment also performs a cascaded summarization process comprising generating a summary of the item using the subportion of the item and a summarization technique, analyzing the summary using a fact-checking algorithm to determine whether the summary satisfies a factual score threshold, and performing a next iteration of the cascaded summarization process while the summary fails to satisfy the factual score threshold. The embodiment also communicates a summary satisfying the factual score threshold to a user device.

Abstract: In an approach to improve abstract summarization with question and answer rewards embodiments generate, by a question and answer generator, questions and answers corresponding to a generated summary. Further, embodiments evaluate received answers for the generated questions by analyzing received answers associated with the generated summary against answers received for an original summary, and calculate a reward based on the similarity between answers associated with generated summary and the original summary. Additionally, embodiments update the generation model by applying the calculated reward to further train the summary generation model.

Abstract: Ad-hoc table retrieval, including: Representing each of a plurality of tables as a multi-field text document in which: different modalities of the table are represented as separate fields, and a concatenation of all the modalities is represented as a separate, auxiliary field. Receiving a query. Executing the query on the multi-field text documents, to retrieve a list of preliminarily-ranked candidate tables out of the plurality of tables. Calculating an intrinsic table similarity score for each of the candidate tables, based on the query and the auxiliary field. Calculating an extrinsic table similarity score for each of the candidate tables, based on a cluster hypothesis of the candidate tables. Combining: the preliminary rankings, the intrinsic table similarity scores, and the extrinsic table similarity scores, to re-rank the candidate tables.

Abstract: Summarization of customer service dialogs by: receiving, as input, a two-party multi-turn dialog; applying a trained next response prediction (NRP) machine learning model to the received dialog, to determine a level of significance of each utterance in the dialog with respect to performing an NRP task over the dialog; assigning a score to each of the utterances in the dialog, based, at least in part, on the determined level of significance; and selecting one or more of the utterances for inclusion in an extractive summarization of the dialog, based, at least in part, on the assigned scores.

Abstract: An example system includes a processor to receive a summary of a conversation to be generated. The processor can input the summary into a trained summary-grounded conversation generator. The processor can receive a generated conversation from the trained summary-grounded conversation generator.

Abstract: An embodiment for predicting a performance of a query in retrieving multifield documents is provided. The embodiment may include receiving a query from a user. The embodiment may also include retrieving a list of multifield documents from a corpus of documents in response to the query. The embodiment may further include generating a pseudo-effective (PE) reference-list for each field in the corpus of documents. The embodiment may also include executing one or more existing query performance prediction (QPP) methods on the retrieved list and each generated PE reference-list. The embodiment may further include deriving one or more extended QPP methods. The embodiment may also include estimating a performance of the query in obtaining the retrieved list of multifield documents based on the one or more extended QPP methods.

Abstract: An embodiment for predicting a performance of a query in retrieving multifield documents is provided. The embodiment may include receiving a query from a user. The embodiment may also include retrieving a list of multifield documents from a corpus of documents in response to the query. The embodiment may further include generating a pseudo-effective (PE) reference-list for each field in the corpus of documents. The embodiment may also include executing one or more existing query performance prediction (QPP) methods on the retrieved list and each generated PE reference-list. The embodiment may further include deriving one or more extended QPP methods. The embodiment may also include estimating a performance of the query in obtaining the retrieved list of multifield documents based on the one or more extended QPP methods.

Abstract: In a computerized information retrieval system: executing a search based on a query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; selecting, from the retrieved tables, a predefined number of highest-ranking tables; scoring each column in the highest-ranking tables using a link analysis algorithm, and selecting, from the scored columns, a predefined number of highest-scoring columns; scoring terms contained within each of the highest-scoring columns, and selecting, from the scored terms, a predefined number of highest-scoring terms; re-ranking the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and providing, as a response to the query, at least one of: the re-ranked tables, ordered according to the re-ranking, and data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

Abstract: An embodiment calculates a point of view (POV) value for a subportion of an item of media content, including evaluating user inputs related to an automatically detected POV of the subportion of the item. The embodiment also calculates a non-factual cost for the subportion of the item indicative of an amount of the subportion of the item that lacks support in a fact-based corpora. The embodiment also performs a cascaded summarization process comprising generating a summary of the item using the subportion of the item and a summarization technique, analyzing the summary using a fact-checking algorithm to determine whether the summary satisfies a factual score threshold, and performing a next iteration of the cascaded summarization process while the summary fails to satisfy the factual score threshold. The embodiment also communicates a summary satisfying the factual score threshold to a user device.

Abstract: In a computerized information retrieval system: executing a search based on a query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; selecting, from the retrieved tables, a predefined number of highest-ranking tables; scoring each column in the highest-ranking tables using a link analysis algorithm, and selecting, from the scored columns, a predefined number of highest-scoring columns; scoring terms contained within each of the highest-scoring columns, and selecting, from the scored terms, a predefined number of highest-scoring terms; re-ranking the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and providing, as a response to the query, at least one of: the re-ranked tables, ordered according to the re-ranking, and data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

Abstract: Automated keyphrase extraction from a digital text document. A pool of candidate keyphrases of the digital text document is created. A cross-entropy method is then employed to compute a set of output keyphrases out of the pool of candidate keyphrases, by iteratively optimizing an objective function that is configured to cause the set of output keyphrases to be descriptive of one or more main topics discussed in the digital text document. The set of output keyphrases may be used for at least one of: text summarization, text categorization, opinion mining, and document indexing.

Abstract: A method, computer system, and computer program product for generating a multi-document summary is provided. The embodiment may include receiving a query statement, one or more documents, one or more summary constraints, and quality goals. The embodiment may include identifying one or more keywords within the query statement. The embodiment may include performing a sentence selection from the one or more documents based on the one or more identified keywords. The embodiment may include generating a plurality of candidate summaries of the one or more documents based on the performed sentence selection, the goals, and a cross entropy method. The embodiment may include calculating a quality score for each of the plurality of generated candidate summaries using a plurality of quality features. The embodiment may include selecting a candidate summary from the plurality of generated candidate summaries with the highest calculated quality score that also satisfies a quality score threshold.

Abstract: Multimodal table encoding, including: Receiving an electronic document that contains a table. The table includes multiple rows, multiple columns, and a schema comprising column labels or row labels. The electronic document includes a description of the table which is located externally to the table. Next, operating separate machine learning encoders to separately encode the description, schema, each of the rows, and each of the columns of the table, respectively. The schema, the rows, and the columns are encoded together with end-of-column tokens and end-of-row tokens that mark an end of each column and row, respectively. Then, applying a machine learning gating mechanism to the encoded description, encoded schema, encoded rows, and encoded columns, to produce a fused encoding of the table, wherein the fused encoding is representative of both a structure of the table and a content of the table.

Abstract: Ad-hoc table retrieval, including: Representing each of a plurality of tables as a multi-field text document in which: different modalities of the table are represented as separate fields, and a concatenation of all the modalities is represented as a separate, auxiliary field. Receiving a query. Executing the query on the multi-field text documents, to retrieve a list of preliminarily-ranked candidate tables out of the plurality of tables. Calculating an intrinsic table similarity score for each of the candidate tables, based on the query and the auxiliary field. Calculating an extrinsic table similarity score for each of the candidate tables, based on a cluster hypothesis of the candidate tables. Combining: the preliminary rankings, the intrinsic table similarity scores, and the extrinsic table similarity scores, to re-rank the candidate tables.

Abstract: A system and a computer-implemented method for ranking tabular data entities by likelihood of comprising answers for (natural language) queries, based on multimodal descriptions of the tabular data entities, comprising separate representations, which represent different aspects of the tabular data entities. The ranking is based on joint representations, generated from the query representation and separate representations of the tabular data entities' aspects, using gated multimodal units. The computer-implemented method may be used for applications such as web searches, data aggregation, and research tasks.

Abstract: Embodiments of the present systems and methods may provide techniques that provide improved information retrieval. For example, a method may comprise receiving, at the computer system, a query to retrieve a document from a corpus of documents, retrieving, at the computer system, a plurality of documents from the corpus of documents using a plurality of retrieval methods, each retrieval method generating a ranked list of retrieved documents and a score for each document, fusing, at the computer system, the generated ranked list of retrieved documents to form an aggregated ranked list of retrieved documents by re-scoring, at the computer system, the plurality of documents according to its passage scores, with respect to the query and associating, at the computer system, a given document and its maximal passage using relevance information induced from the plurality of ranked lists.