Patent Publication Number: US-2023146979-A1

Title: Enhancing natural language processing accuracy in computer systems

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates generally to the field of abstractive summarization and software development, and more particularly to using question and answering rewards to improve abstractive summarization. 
     Abstractive Summarization is a task in Natural Language Processing (NLP) that aims to generate a concise summary of a source text. Unlike extractive summarization, abstractive summarization does not simply copy important phrases from the source text but also potentially come up with new phrases that are relevant, which can be seen as paraphrasing. Abstractive summarization yields a number of applications in different domains, from books and literature to science and research and development (R&amp;D), to financial research and legal documents analysis. Thus, they are not restricted to simply selecting and rearranging passages from the original text. 
     Abstractive methods take advantage of recent developments in deep learning. Since it can be regarded as a sequence mapping task where the source text should be mapped to the target summary, abstractive methods take advantage of the recent success of the sequence to sequence models. These models consist of an encoder and a decoder, where a neural network reads the text, encodes it, and then generates target text. In general, building abstract summaries is a challenging task, which is relatively harder than data-driven approaches such as sentence extraction and involves complex language modeling. 
     SUMMARY 
     Embodiments of the present invention disclose a computer-implemented method, a computer program product, and a system for improving abstract summarization with question and answer rewards in a computer system, the computer-implemented method comprising: generating, by a question and answer generator, questions and answers corresponding to a generated summary; evaluating received answers for the generated questions by analyzing received answers associated with the generated summary against answers received for an original summary; calculating a reward based on a similarity between answers associated with the generated summary and the original summary; and updating a generation model by applying the calculated reward to further train the generation model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1 A  is a functional block diagram illustrating a distributed data processing environment, in accordance with an embodiment of the present invention; 
         FIG.  1 B  is a functional block diagram illustrating a distributed data processing environment of an abstract summarization component, in accordance with an embodiment of the present invention; 
         FIG.  2    illustrates operational steps of the abstract summarization component, on a server computer within the distributed data processing environment of  FIG.  1 A , for improving abstract summarization with question and answer rewards in a computer system, in accordance with an embodiment of the present invention; 
         FIG.  3    depicts an exemplary scenario, in accordance with an embodiment of the present invention; 
         FIG.  4    contains operational steps of the abstract summarization component, for training the summarization framework using QA rewards, in accordance with an embodiment of the present invention; 
         FIG.  5    contains an exemplary algorithm, in accordance with an embodiment of the present invention; 
         FIGS.  6 A- 6 D  depict exemplary tables, in accordance with an embodiment of the present invention; 
         FIG.  7    an exemplary scenario, in accordance with an embodiment of the present invention; and 
         FIG.  8    depicts a block diagram of components of the server computer executing the abstract summarization component within the distributed data processing environment of  FIG.  1 A , in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention recognize that neural abstractive summarization models have improved in the recent years. However, the summaries generated by these models generally suffer from issues such as: misidentifying or missing (i.e., not capturing) the critical facts in source documents, and outputting facts in the generated summaries that are inconsistent with the source documents. Embodiments of the present invention recognize that current reward mechanisms in the art do not improve the factual correctness of summaries as the question answering reward frameworks. 
     Embodiments of the present invention implement a general framework to train abstractive summarization models to alleviate such issues. Embodiments of the present invention improve the art of abstractive summarization and solve the issues outlined above by training a sequence-to-sequence model to summarize documents, and further train the sequence-to-sequence model in a Reinforcement Learning (RL) setting with question-answering based rewards. Further, embodiments of the present invention improve the art by evaluating the summaries generated by the general framework using multiple automatic measures and human judgements. The experimental results, described below, show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization. Particularly, the results from human evaluations show that the summaries generated by embodiments of the present invention are preferred over 30% more over the summaries generated by general abstractive summarization models currently in the art. 
     Implementation of embodiments of the invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures (i.e.,  FIG.  1 A - FIG.  8   ). 
       FIG.  1 A  is a functional block diagram illustrating a distributed data processing environment, generally designated  100 , in accordance with one embodiment of the present invention. The term “distributed” as used in this specification describes a computer system that includes multiple, physically distinct devices that operate together as a single computer system.  FIG.  1 A  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. Distributed data processing environment  100  includes computing device  110  and server computer  120  interconnected over network  130 . 
     Network  130  may be, for example, a storage area network (SAN), a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, a wireless technology for exchanging data over short distances (using short-wavelength ultra-high frequency (UHF) radio waves in the industrial, scientific and medical (ISM) band from 2.4 to 2.485 GHz from fixed and mobile devices, and building personal area networks (PANs) or a combination of the three), and may include wired, wireless, or fiber optic connections. Network  130  may include one or more wired and/or wireless networks that may receive and transmit data, voice, and/or video signals, including multimedia signals that include voice, data, text and/or video data. In general, network  130  may be any combination of connections and protocols that will support communications between computing device  110  and server computer  120 , and any other computing devices and/or storage devices (not shown in  FIG.  1 A ) within distributed data processing environment  100 . 
     In some embodiments of the present invention, computing device  110  may be, but is not limited to, a standalone device, a client, a server, a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a smart phone, a desktop computer, a smart television, a smart watch, a radio, a stereo system, a cloud based service (e.g., a cognitive cloud based service), AR glasses, a virtual reality headset, any HUD known in the art, and/or any programmable electronic computing device capable of communicating with various components and devices within distributed data processing environment  100 , via network  130  or any combination therein. In general, computing device  110  may be representative of any programmable computing device or a combination of programmable computing devices capable of executing machine-readable program instructions and communicating with users of other computing devices via network  130  and/or capable of executing machine-readable program instructions and communicating with server computer  120 . In some embodiments computing device  110  may represent a plurality of computing devices. 
     In some embodiments of the present invention, computing device  110  may represent any programmable electronic computing device or combination of programmable electronic computing devices capable of executing machine readable program instructions, manipulating executable machine-readable instructions, and communicating with server computer  120  and other computing devices (not shown) within distributed data processing environment  100  via a network, such as network  130 . Computing device  110  may include an instance of user interface (interface)  106 , and local storage  104 . In various embodiments, not depicted in  FIG.  1 A , computing device  110  may have a plurality of interfaces  106 . In other embodiments, not depicted in  FIG.  1 A , distributed data processing environment  100  may comprise a plurality of computing devices, plurality of server computers, and/or one a plurality of networks. Computing device  110  may include internal and external hardware components, as depicted, and described in further detail with respect to  FIG.  8   . 
     User interface (interface)  106  provides an interface to abstractive summarization component (component)  122 . Computing device  110 , via user interface  106 , may enable a user and/or a client to interact with component  122  and/or server computer  120  in various ways, such as sending program instructions, receiving program instructions, sending and/or receiving messages, updating data, sending data, inputting data, editing data, collecting data, and/or receiving data. In one embodiment, interface  106  may be a graphical user interface (GUI) or a web user interface (WUI) and may display at least text, documents, web browser windows, user options, application interfaces, and instructions for operation. interface  106  may include data (such as graphic, text, and sound) presented to a user and control sequences the user employs to control operations. In another embodiment, interface  106  may be a mobile application software providing an interface between a user of computing device  110  and server computer  120 . Mobile application software, or an “app,” may be designed to run on smart phones, tablet computers and other computing devices. In an embodiment, interface  106  may enable the user of computing device  110  to at least send data, input data, edit data (annotations), collect data and/or receive data. 
     Server computer  120  may be a standalone computing device, a management server, a web server, a mobile computing device, one or more client servers, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer  120  may represent a server computing system utilizing multiple computers such as, but not limited to, a server system, such as in a cloud computing environment. In another embodiment, server computer  120  may represent a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within distributed data processing environment  100 . Server computer  120  may include internal and external hardware components, as depicted, and described in further detail with respect to  FIG.  8    In some embodiments server computer  120  may represent a plurality of server computers. 
     Each of shared storage  124  and local storage  104  may be a data/knowledge repository and/or a database that may be written and/or read by one or a combination of component  122 , server computer  120  and computing device  110 . In some embodiments, each of shared storage  124  and local storage  104  may be a data/knowledge repository, a knowledge base, a knowledge center, a knowledge corpus, and/or a database that may be written and/or read by one or a combination of component  122 , server computer  120  and computing device  110 . In the depicted embodiment, shared storage  124  resides on server computer  120  and local storage  104  resides on computing device  110 . In another embodiment, shared storage  124  and/or local storage  104  may reside elsewhere within distributed data processing environment  100 , provided that each may access and is accessible by computing device  110  and server computer  120 . Shared storage  124  and/or local storage  104  may each be implemented with any type of storage device capable of storing data and configuration files that may be accessed and utilized by server computer  120 , such as, but not limited to, a database server, a hard disk drive, or a flash memory. In various embodiments, not depicted in  FIG.  1 A , in addition to shared storage  124 , server computer comprises a primary and a secondary database, described below in  FIG.  8   . The primary database, also referred to as primary storage device, may be one or more of any type of disk storage known in the art. The secondary database, also referred to as secondary storage device, may be one or more any type of tape storage known in the art. 
     In the depicted embodiment, component  122  is executed on server computer  120 . In other embodiments, component  122  may be executed on computing device  110 . In various embodiments of the present invention, not depicted in  FIG.  1 A , component  122  may execute on a plurality of server computers  120  and/or on a plurality of computing devices  110 . In some embodiments, component  122  may be located and/or executed anywhere within distributed data processing environment  100  as long as component  122  is connected to and/or communicates with, computing device  110 , and/or server computer  120 , via network  130 . 
     In various embodiments of the present invention, not depicted in  FIG.  1 A , and knowledge corpus  128  may each execute on a plurality of server computers  120  and/or on a plurality of computing devices  110 . In some embodiments, and knowledge corpus  128  may each be located and/or executed anywhere within distributed data processing environment  100  as long as knowledge corpus  128  are connected to and/or communicates with, computing device  110 , component  122 , and/or server computer  120 , via network  130 . 
     Component  122  may alleviate factuality related issues and improve the quality of the abstractive summarization by using question-answering (QA) based rewards. First, component  122  may train a sequence-to-sequence (seq2seq) summary generation model to take a document as the input and generate a summary as the output. Additionally, Component may improve the precision and recall of the summary generation model using a QA framework as follows. To improve the precision of the model, component  122  may generate questions and corresponding answers for each generated summary. In various embodiments, component  122  outputs the generated questions and corresponding answers to a user. In various embodiments, component  122  displays, via interface  106  on computing device  110 , responsive prompts to the user comprising the generated questions and answers that enable a user to select or input a response (e.g., answer). For example, component  122  displays one or more responsive prompts to the user that prompt the user to select a predetermined answer (e.g., multiple choice) or input a response (e.g., essay/short answer response) and/or prompts the user to confirm and save selection of the user to local storage  104  and/or shared storage  124 . In some embodiments, the response from the user is confirmed and/or stored automatically. In various embodiments, component  122  generates and outputs, via interface  106 , responsive prompts that query the user to accept and/or rate the generated/output questions and answers, wherein responsive to receiving feedback from a user component  122  updates the generated questions and answers based on the received user feedback and/or stores the user feedback and generated questions and answers for future use to local storage  104  and/or shared storage  124 . 
     Component  122  may evaluate the received answers for the same or similar questions associated with the ground truth summaries. In various embodiments, if a generated summary contains factually incorrect information, which would result in having different answers from the ground truth summary for some of the generated questions, then component  122  will label the generates summary as irrelevant or non-factual. Component  122  may use the similarity of answers to calculate a reward to improve precision of the summary generation model. Similarly, to improve the recall of the summarization model, component  122  may generate questions and corresponding answers from the ground truth summaries and evaluate the answers component  122  obtains/receives for the same questions from the generated summaries. If the generated summary does not contain some key information as captured in the ground truth summary, then this would lead to having different answers from the ground truth summary for some of the generated questions. Component  122  may use the similarity of answers to calculate a reward to improve the recall. In various embodiments, component  122  may implement the calculated rewards in a Reinforcement Learning (RL) based framework to improve the summary generation model, wherein the textual similarity between the answers are taken as the reward, as illustrated in  FIG.  8   . Component  122  may evaluate the summaries generated by our approach using multiple automatic measures and human judgements, and show that the QA can be used as a general framework to improve abstractive summarization. 
       FIG.  1 B  is a functional block diagram illustrating a distributed data processing environment, generally designated  100 , in accordance with one embodiment of the present invention. The term “distributed” as used in this specification describes a computer system that includes multiple, physically distinct devices that operate together as a single computer system.  FIG.  1 B  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. Distributed data processing environment  100  includes computing device  110 , user  140 , and server computer  120  interconnected over network  130 . 
     In the depicted embodiment, summary generator  406  receives and/or retrieves document  402 , wherein document  402  may be any document containing textual information known and understood in the art. In various embodiments, summary generator  406  may receive document  402  from a user input via interface  106 , or retrieve document  402  from local storage  104  and/or shared storage  124  automatically or based on user instructions received through interface  106 . Summary generator  406  generates generated summary  408  based on the retrieved and/or received document  402 . Further, in the depicted embodiment, question/answer (QA) generator  410  receives generated summary  408  and original summary  404  and generates questions and corresponding answers to the generated questions (i.e., generated questions and answers  411 ) based on the received generated summary  408  and original summary  404 . In various embodiments, QA generator  410  receives original summary  404  from a user input via interface  106 , or retrieves original summary  404  from local storage  104  and/or shared storage  124  automatically or based on user instructions received through interface  106 . QA generator may receive original summary  404  and generated summary  408  automatically through predetermined instructions from component  122  and/or receive instructions from component  122  to retrieve generated summary from summary generator  406  and retrieve original summary  404  from local storage  104 , shared storage  124 , and/or user  140 , via responsive prompt  168  and interface  106 . 
     In the depicted embodiment, component  122  outputs generated questions and answers  411  to user  140 , via responsive prompt  168  on interface  106 . In various embodiments, responsive to receiving one or more responses to the output generated questions and answers  411 , component  122 , via reward model  142  evaluates the received answers for generated questions and answers  411  by analyzing received answers associated with generated summary  408  against answers received for original summary  404 . Reward model  142  may calculate a reward based on the similarity between answers associated with generated summary  408  and original summary  404 . In various embodiments, if the calculated similarity is within or above a predetermined threshold then reward model  142  generates and outs reward  414 . In various embodiments, policy training  416  updates the generation model, via summary generator  406 , by applying the calculated reward (reward  414 ) to further train summary generation model (summary generator)  406 . 
       FIG.  2    illustrates operational steps of component  122 , generally designated  200 , in communication with server computer  120 , within distributed data processing environment  100  of  FIG.  1 A  and/or  FIG.  1 B , for improving abstract summarization with question and answer rewards, in accordance with an embodiment of the present invention.  FIG.  2    provides an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. 
     In step  202 , component  122  trains a generation model. In various embodiments, component  122  trains one or more generation models. Component  122  may train a sequence-to-sequence (seq2seq) summary generation model (i.e., generation model) to take a document as the input and may generate a summary as the output. In various embodiments, component  122  trains the generation model to receive documents as input from a user through interface  106  on computing device  110  and/or automatically and/or direction from computing device  110 , via local storage  104  and/or shared storage  124 . For example, receiving a document upload from a user through a computer or automatically retrieving documents from a predetermined folder on the computer&#39;s hard drive. 
     In step  204 , component  122  generates a summary. In various embodiments, component  122  generates a summary based on the received documents. Component  122  may output the generated summary to a user, via interface  106 . In various embodiments, component  122  may generate questions and corresponding answers for one or more generated summary. 
     In step  206 , component  122  generates questions and answers. In various embodiments, component  122  generates one or more questions and answers. Component  122  displays, via interface  106  on computing device  110 , responsive prompts to the user comprising the generated questions and answers that enable a user to select or input a response (e.g., answer). For example, component  122  displays one or more responsive prompts to the user that prompt the user to select a predetermined answer (e.g., multiple choice) or input a response (e.g., essay/short answer response) and/or prompts the user to confirm and save selection of the user to local storage  104  and/or shared storage  124 . 
     In step  208 , component  122  outputs the generated questions and answers. In various embodiments, component  122  displays, via interface  106  on computing device  110 , responsive prompts to the user comprising the generated questions and answers that enable a user to select or input a response (e.g., answer). For example, component  122  displays one or more responsive prompts to the user that prompt the user to select a predetermined answer (e.g., multiple choice) or input a response (e.g., essay/short answer response) and/or prompts the user to confirm and save selection of the user to local storage  104  and/or shared storage  124 . 
     In step  210 , component  122  evaluates the received answers. In various embodiments, component  122  evaluates the received answers from one or more users. Component  122  may evaluate the answers component  122  obtains/receives for the same questions from the generated summaries. If the generated summary does not contain some key information as captured in the ground truth summary, then this would lead to having different answers from the ground truth summary for some of the generated questions. In various embodiments, component  122  may evaluate the summaries generated by our approach using multiple automatic measures and human judgements, and show that the QA can be used as a general framework to improve abstractive summarization. 
     In step  212 , component  122  calculates a reward. In various embodiments, component  122  calculates one or more rewards based on the evaluation of the received answers. Component  122  may use the similarity of answers to calculate a reward to improve the recall. In various embodiments, component  122  may implement the calculated rewards in a Reinforcement Learning (RL) based framework to improve the summary generation model. 
     In step  214 , component  122  updates the generation model. In various embodiments, component  122  updates the generation model based on the calculated reward and evaluation of the received answers. 
     FURTHER COMMENTS AND/OR EMBODIMENTS 
     Some embodiments of the present invention recognize the following facts, potential problems, and/or potential areas for improvement with respect to the current state of the art. 
     Embodiments of the present invention recognize that neural abstractive summarization models have drastically improved in the recent years. However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents. In this work, embodiments of the present invention present a general framework to train abstractive summarization models to alleviate such issues. Embodiments of the present invention first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning set-ting with question-answering based rewards. Embodiments of the present invention evaluate the summaries generated by this framework using multiple automatic measures and human judgements. The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization. Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models. 
     1 INTRODUCTION 
     Although neural abstractive summarization has seen drastic improvements over the recent years, embodiments of the present invention recognize that these systems still have multiple drawbacks. One such common draw-back is that the generated summaries frequently fail to capture critical facts in source documents (low recall). On the other hand, embodiments of the present invention recognize that neural abstractive summarization models are known to generate content which are inconsistent with the source document (low precision). This is also commonly known as hallucination. Some studies claim that nearly 30% of the outputs of a summarization models suffer from this problem. 
       FIG.  3    shows a source document, the ground truth summary and few summaries generated by neural models. In the Generated Summary 1, the model fails to capture some crucial facts in the original document (e.g., the play is being a translated not performed by Charlee). The original document is the source document, for which the summary is generated. In Generated Summary 2, the model successfully identifies the fact that the play is a translated, however incorrectly mentions that both Charlee and Curtis are performing when Charlee is the only one preparing for the play. Such factuality related issues are common in neural abstractive summarization models, which makes the models hardly usable in real-world applications. In Generated Summary 3, embodiments of the present invention outputs a more accurate summary of the original dialog when compared to Generated Summary 1 and 2. 
     In this work, embodiments of the present invention propose a general framework to alleviate factuality related issues and improve the quality of the abstractive summarization by using question-answering (QA) based rewards. First, embodiments of the present invention train a sequence-to-sequence (seq2seq) summary generation model to take a document as the input and generate a summary as the output. Next, embodiments of the present invention improve the precision and recall of the summary generation model using a QA framework as follows. To improve the precision of the model, embodiments of the present invention first generate questions and corresponding answers for each generated summary. Next, embodiments of the present invention evaluate the answers that embodiments of the present invention get for the same questions from the ground truth summaries. If a generated summary contains factually incorrect information, then this will result in having different answers from the ground truth summary for some of the generated questions. 
     Embodiments of the present invention use the similarity of answers to calculate a reward to improve the precision. Similarly, to improve the recall of the summarization model, embodiments of the present invention generate questions and corresponding answers from the ground truth summaries and evaluate the answers embodiments of the present invention obtain for the same questions from the generated summaries. If the generated summary does not contain some key information as captured in the ground truth summary, then this would lead to having different answers from the ground truth summary for some of the generated questions. Embodiments of the present invention use the similarity of answers to calculate a reward to improve the recall. The calculated rewards were used in a Reinforcement Learning (RL) based framework to improve the summary generation model. In  FIG.  3    embodiments of the present invention show an example output from our approach, which does not contain the factuality related issues shown above. Embodiments of the present invention evaluate the summaries generated by our approach using multiple automatic measures and human judgements, and show that the QA can be used as a general framework to improve abstractive summarization. 
     In summary, our key contributions are: (1) embodiments of the present invention introduce a Reinforcement Learning framework, which uses QA rewards to improve the recall and precision of abstractive summarization. (2) The framework is evaluated on three commonly used transformer based summarization models on two public datasets. (3) The evaluation of generated summaries on several automatic measures and human judgements show the effectiveness of our method. In particular, the human judges prefer summaries generated by our approach more than 30% of the time over the summaries generated by general abstractive summarization models. 
     2 IMPROVING SUMMARIZATION WITH QA REWARDS 
     In general, abstractive summarization models are trained to minimize the cross entropy loss of the reference summary at the word level, which does not necessarily reward models for being factually accurate with high precision and recall. Hence, to improve the factually accuracy of abstractive summarization, component  122  executes a general framework which uses QA based rewards and RL based training. Component  122  executing the proposed framework is illustrated in  FIG.  4   .  FIG.  4    comprises document  402 , original summary  404 , summary generator  406  (i.e., a policy), generated summary  408 , QA generator  410 , reward model  412 , and reward  412 . In the depicted embodiment, summary generator  406  receives document  402  and generates generated summary  408  based on the received document  402 . Further, QA generator  410  receives generated summary  408  and original summary  404  and generates questions and answers based on the received generated summary  408  and original summary  404 . 
     In regard to summary generator  406 , embodiments of the present invention recognizes that recent work in the art has leveraged pretrained Transformer models for abstractive summarization. In various embodiments, component  122 , via summary generator  406 , as the first step of summary generation, component  122  trains a transformer based seq2seq model (S), where the source document is fed as the input and the model is trained to generate the summary token-by-token. The model is trained to optimize cross entropy loss. During inference, component  122  may use top-p nucleus sampling with p=0.95. 
     QA Generator  410  is utilized to generate questions and answers from original summary  404  and generated summary  408 . Component  122 , via QA generator  410 , generates questions and corresponding answers from original summary  404  and evaluates the answers obtained for the generated questions associated with original summary  404  by analyzing (e.g., comparing) them against the answers received from generated summary  408 . Similarly, component  122  may generate questions and corresponding answers from generated summary  406  and may evaluate the obtained answers by analyzing (e.g., comparing) the received questions associated with generated summary  406  with the questions and answers received/obtained from original summary  408 . The functionality of the QA framework is explained in Algorithm  500  depicted in  FIG.  5   . To generate questions and corresponding answers, component  122  may use an answer aware question generation model finetuned on t5-base model. In  FIG.  5   , Algorithm  500  represents the algorithm for the question and answer (QA) framework for factuality reward calculation. The input for Algorithm  500  comprises: trained summarization model (S), Question-Answer Generation Model (QA), Answer Generation Model (A), Input Document (D), Ground Truth Summary (Gt), and Textual Similarity Function (T), wherein the output for Algorithm  500  comprises: Reward value (R) for Generated Summary (Ga). 
     To identify the answer for a generated question from a summary, embodiments of the present invention use an extractive QA model trained on the SQuAD task. In regard to reward model  412 , component  122 , via reward model  412 , may use the similarity between the answers obtained by generated summary  408  and the ground truth summary (e.g., original summary  404 ) as the reward function. Generated summary  408  is considered relevant if the questions posed by the ground truth summary (e.g., original summary  404 ) can be answered correctly by generated summary  408 , as this shows the critical information queried by the question is present in generated summary  408 . Similarly, generated summary  408  is considered factual if a question generated on generated summary  408  can be correctly answered original summary  404 , as the questions generated on a hallucinated summary will not be correctly answered by original summary  404 . Component  122  may use the Normalized Levenshtein distance as the similarity measure. An example for using QA for reward calculation is provided in Section 5 below and  FIG.  8   . The reward obtained from Algorithm  500  is used in the reinforcement learning (RL) framework (shown in  FIG.  4   ) to further train the summary generation model S. 
     In regard to policy training  416 , component  122 , via policy training  416 , use proximal policy optimization (ppo) as the optimizer for the policy training, as it prevents the generator from moving too far away from the pretrained language model. Embodiments of the present invention recognize that the approach of QA based optimization following general seq2seq training, described above, enables the described framework to be applicable across different abstractive summarization models. 
     3 EVALUATION AND RESULTS 
     Component  122  may evaluate the QA based summarization framework on three common neural abstractive summarization models GPT-2, BART, and PEGASUS and on two different abstractive summarization datasets: (1) XSUM: consists of 227 k news articles covering a wide variety of subjects along with human written single-sentence summaries, and (2) SAMSUM: conversation summarization dataset, containing over 13 k open-domain conversations and summaries created by humans. Component  122  may evaluate each model, first, with general method of training: generate the summary given the document, then, with further RL based training with QA rewards that embodiments of the present invention propose. The hyper-parameters used in training are available in the Sections 4-4.4 below. 
     In relation to evaluations with ROUGE scores, component  122  may first evaluate the models using the ROUGE scores. The obtained results are reported in  FIGS.  6 A and  6 B . Each table in  FIG.  6 A  and  FIG.  6 B  contains two sections, where the first section shows the accuracy before training with QA based rewards, and the second section shows the results after RL based training with QA rewards. The results clearly suggest that for both datasets, each model improves its summarization accuracy using our QA framework. 
     In regard to factuality based evaluations, in various embodiments, component  122  evaluates the results obtained from our models in a factuality based evaluation framework. This measure provides better correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), and provides precision, recall and F1 for a generated summary given a reference, wherein F1 is the harmonic mean of the precision and recall. The results obtained on the two datasets are shown in  FIG.  6 C . Similarly, to the ROUGE based evaluation, the results here clearly indicate that for both datasets, each model improves its accuracy using our QA framework. 
     In relation to human evaluation, component  122  may further conducted human evaluations to study the quality of the models. Component  122  may focus on the two models that obtained the best scores in our automatic evaluations: Pegasus and BART, and compared the quality of summaries between the original model to our model optimized with QA rewards. In the depicted embodiments, component  122  followed the evaluation protocol, in which annotators were presented with a document, a ground truth summary and a model summary and asked to make two decisions: (1) which model summary is more factual consistent with the given document, and (2) which model summary is of a higher quality, taking into account Informativeness, Fluency, and Succinctness. Annotators were presented with the models summaries, in a randomized order, and were instructed to select one of them or indicate that both are equally good or bad. 
     For this assessment component  122  first randomly sampled 30 records from the test sets of SAMSUM and XSUM (overall 60 records). Then, component  122  generated 4 types of summaries: Pegasus, Pegasus-QA, BART, BART-QA. To achieve a high quality standard embodiments of the present invention recruited 6 NLP experts, and collected three human judgments for each summary. To obtain a single score per summary, component  122  took the majority vote of the collected assessments.  FIG.  6 D  describes the results of this assessment. The values represent the number of times that a model was selected as strictly better than its counterpart out of 30 annotated summaries. Differences between QA based reward generation model to the original model is statistically significant (with p&lt;0.05). These results indicate that QA based rewards helps to significantly improve summary generation model, considering both factual consistency and general quality aspects. 
     4 MODEL TRAINING AND HYPERPARAMETER DETAILS 
     In this section, embodiments of the present invention elaborate the training processes and the hyperparameters used by the models used in this study. Each experiment was run on 2 V100 GPUs (on a single machine). 
     4.1 GPT2 Model 
     Embodiments of the present invention fine-tune a GPT-2 language model for this task by using the implementation available at HuggingFace. The hyperparameters used during training and inference are shown below in language model hyperparameters. The model takes around 3 hours to train for the SAMSUM data and approximately 24 hours to train on the XSUM data. Embodiments of the present invention finetune this on XSUM and SAMSUM datasets in respective applications. Language model hyperparameters for HuggingFace comprise: 
     model_name: gpt2;
 
per_gpu_train_batch_size: 4;
 
per_gpu_eval_batch_size: 4;
 
gradient_accumulation_steps: 4;
 
learning_rate: 6.25e-5;
 
adam_epsilon: 1e-8;
 
max_grad_norm: 1.0;
 
num_train_epochs: 10;
 
warmup_steps: 500; and
 
max_input_tokens: 512.
 
     4.2 BART Model 
     Embodiments of the present invention used a BART model (Lewis et al., 2019) provided by HuggingFace library, which is fine-tuned on the extreme summarization (XSUM) task. During the evaluation with SAM-SUM dataset, embodiments of the present invention further fine-tune this model on SAMSUM data. This model takes around 6 hours to finetune on the SAMSUM data. The code used for the fine-tuning is publicly available. The hyperparameters used for training the BART model are as follows: 
     train_batch_size=4;
 
eval_batch_size=4;
 
num_train_epochs=10;
 
model_name=facebook/bart-large-xsum;
 
learning_rate=3e-5;
 
val_check_interval=0.1;
 
max_source_length=512; and
 
max_target_length=80.
 
     4.3 PEGASUS Model 
     Similar to the BART experiments, embodiments of the present invention use a PEGASUS model provided by HuggingFace library, which is fine-tuned on the extreme summarization (XSUM) task. During the evaluation with SAMSUM dataset, embodiments of the present invention further fine-tune this model on SAMSUM data. This model takes around 7 hours to finetune on the SAMSUM data. The code used for the fine-tuning is publicly available. The hyperparameters used for training the PEGASUS model are as follows: 
     train_batch_size=4;
 
eval_batch_size=4;
 
num_train_epochs=10;
 
model_name=google/pegasus-xsum;
 
learning_rate=3e-5;
 
val_check_interval=0.1;
 
max_source_length=512; and
 
max_target_length=80.
 
     4.4 Reinforced Learning Model with QA Rewards 
     Embodiments of the present invention adapted a publicly available Proximal Policy Optimization (PPO) implementation 9 for the RL model with QA rewards. The model was trained for 10000 steps and takes around 12 hours to train. Following hyper-parameters were used to train the model, as follows: 
     steps: 100000;
 
batch_size: 16;
 
forward_batch_size: 4;
 
learning_rate: 1.41e-5;
 
init_kl_coef:0.2;
 
target: 6;
 
horizon:10000;
 
gamma:1;
 
lam:0.95;
 
cliprange: 0.2;
 
cliprange_value: 0.2; and
 
vf_coef: 0.1.
 
     5 EXAMPLE—REWARD CALCULATION WITH QUESTION-ANSWERS 
     In  FIG.  7   , embodiments of the present invention provide an example for calculating rewards with QA. The figure first shows a document, which is represented by original document (i.e., dialog)  700 , with its corresponding ground truth (GT) summary  702  and abstractive summary generated (GEN)  704  by the BART based summarization model. Then the next section shows the QA pairs generated by the GT summary  706  and the answers obtained by the GEN summary for the same questions. For example, for the question ‘Who will visit person_1&#39;s grandma tonight?’, the answer from the GT summary is ‘person_1 and person_0’ while the answer from the GEN summary is only ‘person_1’. Since the model failed to capture the fact that both persons will be visiting grandma, the model will receive a lower reward for this case, as shown by the similarity score (i.e., similarity). In another example, for the question ‘Who will buy her some chocolate?’, the answer from the GT summary is ‘person_0’, which is the answer from the GEN summary. Thus, the answer from the GT summary and GEN summary are a match, resulting in a similarity score (similarity) of 1.0. The next section, of the depicted embodiment, shows the questions and answers generated from the GEN summary  708 . For example, In another example, for the question ‘What will person_0 buy for her?’, the GEN summary produces the answer ‘chocolate and cake’ while the GT summary produces ‘chocolate’ as the answer. This mismatch occurs since GEN summary has some hallucinated content (cake), and this will be penalized with a lower reward during the RL model training. 
     6 CONCLUSION 
     In various embodiments, the problem of low recall and precision of factuality in neural abstractive summarization models was investigated and a proposed framework to alleviate the identified problems above by using question-answering (QA) based rewards. The proposed framework is evaluated on three commonly used transformer based summarization models and on two publicly avail-able datasets. The automatic evaluations were performed using ROUGE scores as well as question answering based evaluation framework and the results suggest that the proposed method improves the summarization accuracy and factuality. The human evaluation on the generated summaries also suggest that our approach produces summaries with significantly high factual consistency and quality. 
       FIG.  3    contains exemplary scenario, in accordance with an embodiment of the present invention. More specifically,  FIG.  3    illustrates a document, its corresponding ground truth summary, and model generated summaries, in accordance with an embodiment of the present invention. 
       FIG.  4    contains operational steps of component  122 , on a server computer within the distributed data processing environment of  FIG.  1 A , for training the summarization framework using QA rewards, in accordance with an embodiment of the present invention. 
       FIG.  5    contains exemplary algorithm  500 , in accordance with an embodiment of the present invention. 
       FIG.  6 A  contains exemplary table  602  describing abstractive summarizers on SAMSUM, in accordance with an embodiment of the present invention. 
       FIG.  6 B  contains exemplary table  604  describing abstractive summarizers on XSUM, in accordance with an embodiment of the present invention. 
       FIG.  6 C  contains exemplary table  606  describing the results of a QA based evaluation, in accordance with an embodiment of the present invention. 
       FIG.  6 D  contains exemplary table  608  describing the results of human evaluation, in accordance with an embodiment of the present invention. 
       FIG.  7    contains exemplary scenario, in accordance with an embodiment of the present invention. More specifically,  FIG.  7    illustrates an example of reward calculation with Question-Answer pairs. 
       FIG.  8    depicts a block diagram of components of server computer  120  within distributed data processing environment  100  of  FIG.  1 A  and/or  FIG.  1 B , in accordance with an embodiment of the present invention. It should be appreciated that  FIG.  8    provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
       FIG.  8    depicts computer system  800 , where server computing  120  represents an example of computer system  800  that includes component  122 . The computer system includes processors  801 , cache  803 , memory  802 , persistent storage  805 , communications unit  807 , input/output (I/O) interface(s)  806 , display  809 , external device(s)  808  and communications fabric  804 . Communications fabric  804  provides communications between cache  803 , memory  802 , persistent storage  805 , communications unit  807 , and input/output (I/O) interface(s)  806 . Communications fabric  804  may be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications, and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  804  may be implemented with one or more buses or a crossbar switch. 
     Memory  802  and persistent storage  805  are computer readable storage media. In this embodiment, memory  802  includes random access memory (RAM). In general, memory  802  may include any suitable volatile or non-volatile computer readable storage media. Cache  803  is a fast memory that enhances the performance of processors  801  by holding recently accessed data, and data near recently accessed data, from memory  802 . 
     Program instructions and data used to practice embodiments of the present invention may be stored in persistent storage  805  and in memory  802  for execution by one or more of the respective processors  801  via cache  803 . In an embodiment, persistent storage  805  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  805  may include a solid-state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  805  may also be removable. For example, a removable hard drive may be used for persistent storage  805 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  805 . 
     Communications unit  807 , in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit  807  includes one or more network interface cards. Communications unit  807  may provide communications through the use of either or both physical and wireless communications links. Program instructions and data used to practice embodiments of the present invention may be downloaded to persistent storage  805  through communications unit  807 . 
     I/O interface(s)  806  enables for input and output of data with other devices that may be connected to each computer system. For example, I/O interface  806  may provide a connection to external devices  808  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  808  may also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention may be stored on such portable computer readable storage media and may be loaded onto persistent storage  805  via I/O interface(s)  806 . I/O interface(s)  806  also connect to display  809 . 
     Display  809  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium may be any tangible device that may retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein may be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general-purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that may direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures (i.e., FIG.) illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, may be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.