Patent Publication Number: US-2023153341-A1

Title: Using neural networks to detect incongruence between headlines and body text of documents

Description:
TECHNICAL FIELD 
     This disclosure generally relates to machine learning techniques for documents analysis. More specifically, but not by way of limitation, this disclosure relates to machine learning models for detection of documents having an incongruent headline. 
     BACKGROUND 
     Content provider systems that host articles or other documents can be configured to detect documents that exhibit headline incongruence, where some or all of the body text of the document does not logically correspond to the headline. 
     SUMMARY 
     The present disclosure describes techniques for using graph neural networks (GNNs) to identify documents having headline incongruence, wherein the identification can be used for modifying online computing environments or other systems. For example, an incongruent headline detection system receives a request to determine a headline incongruence score for an electronic document. The incongruent headline detection system determines the headline incongruence score for the electronic document by applying a machine learning model to the electronic document. Applying the machine learning model to the electronic document comprises generating a graph representing a textual similarity between a headline of the electronic document and each of a plurality of paragraphs of the electronic document and determining the headline incongruence score using the graph. The incongruent headline detection system transmits, responsive to the request, the headline incongruence score for the electronic document. 
     Various embodiments are described herein, including methods, systems, non-transitory computer-readable storage media storing programs, code, or instructions executable by one or more processors, and the like. These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features, embodiments, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings. 
         FIG.  1    depicts an example of a computing environment for determining a headline incongruence prediction for an electronic document, according to certain embodiments disclosed herein. 
         FIG.  2    depicts an example of a process for determining a headline incongruence prediction for an electronic document, according to certain embodiments disclosed herein. 
         FIG.  3    depicts an example illustration of a headline incongruence detection model for implementing certain embodiments disclosed herein. 
         FIG.  4    depicts an example illustrations of headline-incongruent electronic documents, according to certain embodiments disclosed herein. 
         FIG.  5    depicts an example of a process for training a headline incongruence detection model, according to certain embodiments disclosed herein. 
         FIG.  6    depicts an example of a process for generating training data for training a headline incongruence detection model, according to certain embodiments disclosed herein. 
         FIG.  7    depicts an example illustration of generating training data from a set of headline-congruent documents, according to certain embodiments disclosed herein. 
         FIG.  8    depicts an example illustration of a headline-incongruent electronic document and of a graph representing the headline-incongruent electronic document, according to certain embodiments disclosed herein. 
         FIG.  9    depicts an example of a computing system that performs certain operations described herein, according to certain embodiments described in the present disclosure. 
         FIG.  10    an example of a cloud computing system that performs certain operations described herein, according to certain embodiments described in the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of certain embodiments. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. The words “exemplary” or “example” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or design described herein as “exemplary” or “example” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. 
     Conventional models for detecting headline incongruence in documents focus on learning a relationship between a headline and the whole of the body text. For example, conventional models may learn characteristics of the document headline and a body text jointly via a neural network. However, since body texts of documents can be thousands of words in length, conventional models provide less-accurate predictions of headline incongruence due to increased content complexity that occurs as a length of the document is increased. Also, conventional models for detecting headline incongruence in documents are trained using document data sets that are manually annotated using ground truth labels. However, manually annotating each document of a data set (e.g. with a headline congruent label or a headline-incongruent label) practically limits a size of a training data set, which decreases an accuracy of headline incongruence predictions generated by conventional models. 
     Certain embodiments described herein address the limitations of conventional systems by providing a machine learning model to automatically identify documents that exhibit an incongruence between a headline text and a body text (a headline incongruence), wherein the identification can be used for modifying online computing environments or other systems. Certain embodiments described herein improve the performance of automated document classification systems by, for example, determining a headline incongruence prediction for an electronic document based on a textual similarity between each paragraph of the electronic document to a headline of the electronic document. Thus, the prediction of the graph-based model approach described herein can be more accurate or useful than those provided by conventional solutions, particularly as the body text length of documents is increased. Further, certain embodiments described herein address the limitations of conventional systems by providing a method to automatically generate a training data set of documents that are labeled with ground truth labels, which increases a size of a training data set that can be used for training a headline incongruence prediction model, which provides for predictions that are more accurate than those provided by conventional solutions, which rely on manually-annotated training document sets. 
     The following non-limiting example is provided to introduce certain embodiments. In this example, an incongruent headline detection system implementing a headline incongruence detection model (e.g. a graph-based hierarchical dual encoder network) receives input data including an electronic document. The incongruent headline detection system implementing the headline incongruence detection model determines a headline incongruence prediction for the electronic document that describes a level of incongruence between the headline and body text of the electronic document. For example, the incongruent headline detection system receives an electronic document (e.g. a news article) and a request for a headline incongruence prediction (e.g. a headline incongruence score or a headline incongruence label) for the electronic document. The electronic document includes a headline text and a body text that includes a number of paragraphs. 
     The incongruent headline detection system applies a headline incongruence detection model to the input electronic document to determine a headline incongruence prediction for the input electronic document. In some embodiments, the headline incongruence prediction includes a headline incongruence score that describes a degree of headline incongruence of the document. In some embodiments, the headline incongruence prediction includes a headline incongruence label. In some embodiments, the headline incongruence detection model determines a headline incongruence prediction for each paragraph of the document and determines the headline incongruence prediction for the document based on the paragraph-specific headline incongruence predictions. For example, the headline incongruence prediction for the document is a function of the paragraph-specific headline incongruence predictions. 
     In some examples, the headline incongruence detection model includes a hierarchical dual encoder, an edge learning model, a feature propagation model, and an incongruity prediction model. The hierarchical dual encoder is configured to determine, for each of the headline (i.e. the headline text) and the paragraphs (i.e. the respective paragraph texts), a respective hidden state. The edge learning model is configured to determine a textual similarity between the headline and each of the paragraphs of the electronic document. The edge learning model is configured to determine edge weights between the headline and each of the paragraphs, the edge weights representing a textual similarity between the headline and each of the paragraphs. The feature propagation model is configured to generate a graph for the headline and the paragraphs of the electronic document based on the hidden states and the textual similarities. The graph includes a headline node associated with the headline of the electronic document, a set of paragraph nodes associated with the paragraphs of the electronic document, and edges connecting the headline node to each paragraph node of the graph. The feature propagation model assigns, to the headline node and each of the paragraph nodes, the hidden states associated with the headline and paragraphs as determined by the hierarchical dual encoder. The feature propagation model further assigns, to each edge connecting the headline node to a respective paragraph node, the edge weight determined by the edge learning model that represents the textual similarity between the respective paragraph (associated with the respective paragraph node) and the headline (associated with the headline node). The feature propagation model iteratively applies a feature propagation algorithm on the graph to propagate node features (e.g., hidden states) into neighboring nodes of the graph according to the graph structure (e.g., connections defining edge weights between nodes). Based on the headline node features and paragraph node features of the updated graph, the incongruity prediction model determines an incongruence prediction for each of the paragraphs of the electronic document. The incongruity prediction model determines overall headline incongruence prediction for the electronic document based on the headline incongruence predictions for each of the paragraphs. For example, the overall headline incongruence prediction can be determined as a linear or non-linear function of the headline incongruence predictions for each of the paragraphs. 
     Continuing with this example, the incongruent headline detection system transmits the headline incongruence prediction (e.g. score or label) predicted by the headline incongruence detection model for the electronic document to the system from which the request to determine the headline incongruence prediction was received. In some embodiments, the headline incongruence detection system, or the system to which the headline incongruence prediction is transmitted, modifies features of an online computing environment based on the prediction of headline incongruence by the headline incongruence detection model for one or more electronic documents. In some instances, modifying the features of the online computing environment include deleting, limiting access to, labeling, or otherwise performing an action with respect to the electronic document associated with the headline incongruence prediction. 
     In certain embodiments, during a training phase, the headline incongruence detection model is trained to generate a headline incongruence prediction for an electronic document. In an example, the training data used for the training includes a set of headline-congruent documents. The headline incongruence detection system may generate a set of headline-incongruent documents based on the set of headline-congruent documents. In an example, to generate a headline-incongruent document, the headline incongruence detection system retrieves a first headline-congruent document. The first headline-congruent document has a headline and paragraphs. The headline incongruence detection system retrieves a second headline-congruent document that has a headline and paragraphs. The headline incongruence detection system replaces, in the first headline-congruent document, one or more paragraphs with one or more paragraphs from the second headline-congruent document. In this example, the headline-incongruent document includes the original headline of the first headline-congruent document, one or more paragraphs of the second headline-congruent document that replaced paragraphs of the first-headline congruent document, and remaining paragraphs of the first headline-congruent document that were not replaced with paragraphs from the second headline-congruent document. In some instances, the training data includes the set of headline-congruent documents and the generated set of headline-incongruent documents. 
     In certain embodiments, multiple loss functions are used for training the graph-based neural network. In some instances, the multiple loss functions include a headline incongruence loss function used to minimize an error between generated document-level headline incongruence prediction and ground truth values. In some instances, the headlines and paragraphs in the headline-incongruent documents of the set of training data are labeled so that the headline incongruence detection system can log which paragraphs in a training document are congruent to the headline of the training document. In some instances, the multiple loss functions include an edge loss function providing feedback for determining, by the edge learning model, edge weights between the headline hidden state and each of the paragraph hidden states. In some instances, the edge loss function guides the edge learning model in learning edge weights such that edges of headline-congruent paragraphs are retained and edges of headline-incongruent paragraphs are masked. In some instances, a combined loss function is constructed as a function of the multiple loss functions and is used to train the headline incongruence detection model. 
     The headline incongruence detection model that generates headline incongruence predictions for electronic documents, described herein, provides several improvements and benefits over conventional techniques. In contrast to conventional techniques discussed above that focus on comparing a body text of a document as a single unit to a headline of the document, the headline incongruence detection model described herein enables a more accurate prediction of headline incongruence. This higher accuracy is achieved by considering a headline congruence for each paragraph of the body text, whereas conventional techniques do not consider a paragraph-level headline incongruence. Also, the headline incongruence detection model, as described herein, is more accurate than conventional systems, because it includes constructing a graph structure of the document to enable feature propagation between a headline node and the paragraph nodes, which are each connected to the headline node. The propagation of features between paragraph nodes and the headline node provides for a prediction of headline incongruence with greater accuracy when compared to conventional systems. Further, the headline incongruence detection model described herein provides for predictions with greater accuracy over conventional systems due to its improved training. The headline incongruence detection model described herein can be trained on an automatically generated set of headline-incongruent electronic documents from a set of headline-congruent electronic documents. This training data provides headline incongruence predictions of greater accuracy over conventional systems, which rely on training data sets including documents must first be manually labeled. 
     As used herein, the term “headline congruence” is used to refer to a correspondence, based on a textual similarity, between a headline, title, or other principal section of an electronic document and other text of (e.g., the whole body text or a paragraph thereof) the electronic document that is not part of the principal section. Accordingly, as used herein, the term “headline-congruent electronic document” is used to refer to an electronic document in which a correspondence exists between the headline and one or more other text of the electronic document and the term “headline-incongruent electronic document” is used to refer to an electronic document in which a correspondence does not exist between a headline and one or more other text of the electronic document. One example of a headline-incongruent electronic document is an electronic document in which the headline of the electronic document makes claims that only partially represent a story represented in the body text of the electronic document. Another example of a headline-incongruent electronic document is an electronic document in which the headline of the electronic document is distinct from a main story recited in the body text of the electronic document. 
     Example Operating Environment for Determining Headline Incongruence Predictions for Electronic Documents 
     Referring now to the drawings,  FIG.  1    depicts an example of a computing environment  100  for determining, by an incongruent headline detection system  102 , a headline incongruence prediction  122  for an electronic document  120 . The computing environment  100  includes an incongruent headline detection system  102 , which can include one or more processing devices that execute a detection subsystem  104  for applying a headline incongruence detection model  109  to an electronic document  120  to determine a headline incongruence prediction  122  for the electronic document  120 . The one or more processing devices of the incongruent headline detection system  102  can further execute a model training subsystem  106  for training, using a training module  108  of the model training subsystem  106 , the headline incongruence detection model  109  that is used for generating the headline incongruence prediction  122 . The model training subsystem  106  can further generate, using a training data generator module  107  and from a document data set  112 , a training data set  114  for use by the training module  108  to train the headline incongruence detection model  109 . The document data set  112  could be a set of headline-congruent electronic documents, for example, a set of news articles from a reputable news publisher. The training data set  114  is a set of headline-incongruent electronic documents generated from the headline-congruent document data set  112 . The computing environment  100  further includes a data store  110  for storing data used in the determination of the headline incongruence prediction  122 , such as the training data set  114  and the document data set  112 . 
     The detection subsystem  104  and the model training subsystem  106  may be implemented using software (e.g., code, instructions, program) executed by one or more processing units (e.g., processors, cores), hardware, or combinations thereof. The software may be stored on a non-transitory storage medium (e.g., on a memory device). The computing environment  100  depicted in  FIG.  1    is merely an example and is not intended to unduly limit the scope of claimed embodiments. One of the ordinary skill in the art would recognize many possible variations, alternatives, and modifications. For example, in some implementations, the incongruent headline detection system  102  can be implemented using more or fewer systems or subsystems than those shown in  FIG.  1   , may combine two or more subsystems, or may have a different configuration or arrangement of the systems or subsystems. 
     The detection subsystem  104  is configured to receive or otherwise access an electronic document  120 . In some instances, the electronic document  120  may be provided to the detection subsystem  104  by another system (e.g. a search system, a content provider system, a social network system). In some instances, the detection subsystem  104  receives a request to determine a headline incongruence prediction  122  for the electronic document  120  along with the electronic document  120 . In some instances, the detection subsystem  104  receives the request to determine a headline incongruence prediction  122  along with an identifier (e.g. a publication number, a web address, or other identifier) identifying an electronic document  120 . The detection subsystem  104  can retrieve, responsive to receiving the request, the electronic document  120  associated with the identifier from a database. The electronic document  120  includes a headline text as well as a plurality of paragraph texts. The headline text may be a text of a principal section of the document (e.g., a title text) or may be a hyperlink text of a hyperlink via which the electronic document  120  may be accessed. In some instances, the electronic document  120  includes an article. 
     To generate the headline incongruence prediction  122 , the detection subsystem  104  employs a headline incongruence detection model  109 . Additional details about generating the headline incongruence prediction  122  by applying a trained headline incongruence detection model  109  are provided below with respect to  FIG.  2   . 
     The incongruent headline detection system  102  generates and trains the headline incongruence detection model  109  using the model training subsystem  106 . The model training subsystem  106  builds and trains the headline incongruence detection model  109 . In  FIG.  1   , the model training subsystem  106  includes a training data generator module  107  and a training module  108 . 
     The training data generator module  107  is configured to generate a training data set  114  from a document data set  112 . For example, the training data generator module  107  accesses the document data set  112 , which includes a set of electronic documents known to be headline-congruent (e.g., from a source, such as a reputable news website, known to publish electronic documents that are headline-congruent), and generates a set of headline-incongruent electronic documents by mixing paragraphs and headlines of the electronic documents in the document data set  112 . In some instances, the training data set  114  includes the generated set of headline-incongruent electronic documents. In some instances, the training data set  114  includes both the generated set of headline-incongruent electronic documents and the set of headline-congruent electronic documents (the document data set  112 ). In some instances, the training data generator module  107  labels the paragraphs of the headline-incongruent electronic documents as either headline-congruent (if the paragraph was originally in the electronic document) or non-headline-congruent (if the paragraph was originally in another electronic document and was moved to the current electronic document during generation of the training data set  114 ). 
     The training module  108 , using the training data set  114 , trains the headline incongruence detection model  109  to minimize one or more loss functions. Additional details of generating and training a headline incongruence detection model  109  is described in  FIG.  3   . In various examples, the training data generator module  107  and the training module  108  can be implemented as one or more of program code, program code executed by processing hardware (e.g., a programmable logic array, a field-programmable gate array, etc.), firmware, or some combination thereof. 
     Examples of Computer-Implemented Operations for Determining Headline Incongruence Predictions for Electronic Documents 
       FIG.  2    depicts an example of a process for using a headline incongruence detection model  109  to generate a headline incongruence prediction  122  for an electronic document  120 . One or more computing devices (e.g., the incongruent headline detection system  102  or the individual modules contained therein) implement operations depicted in  FIG.  2   . For illustrative purposes, the process  200  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     At block  210 , the method  200  involves receiving a request to determine a headline incongruence prediction  122  for an electronic document  120 . For example, the incongruent detection subsystem  104  receives an electronic document  120  and a request for a headline incongruence prediction  122  for the electronic document. The electronic document  120  includes a headline text and a body text (e.g., text of the document that is not part of the headline). The body text includes a plurality of paragraphs. In some instances, the headline text includes a text displayed in a link on a website, where selection of the link would cause the electronic document  120  to be displayed via a computing device of a viewer of the document who selects the link using the computing device. In some instances, the incongruent detection subsystem  104  receives the electronic document  120  and the request from a search system, a content provider system (e.g. a news article aggregator website, a social media system, etc.), a document database system, or other system interested in the headline incongruence prediction  122  for the electronic document  120 . In some instances, the incongruent detection subsystem  104  periodically scans a document database of a system (e.g. a search system, content provider system, or other interested system) to retrieve electronic documents  120  and provide headline incongruence predictions  122  for the retrieved electronic documents  120  to the system associated with the document database. 
     At block  220 , the method  200  involves generating the headline incongruence prediction  122  by applying a headline incongruence detection model  109  to the electronic document  120 . In some embodiments, the headline incongruence prediction  122  is a headline incongruence score that describes a degree of headline incongruence of the electronic document  120 . For example, the score is a value between 0 and 1, wherein a value closer to 0 is more headline-incongruent than a value farther away from 0 and wherein a value closer to 1 is more headline-congruent than a value farther away from 1. In some embodiments, the headline incongruence prediction  122  is a headline incongruence label. For example, the label is either is a headline-congruent label identifying the electronic document  120  as being headline-congruent or is a headline-incongruent label identifying the electronic document  120  as being headline-incongruent. In some embodiments, the headline incongruence detection model  109  determines a headline incongruence prediction  122  for each paragraph of the electronic document  120  and determines the headline incongruence prediction  122  for the electronic document  120  based on the paragraph-specific headline incongruence predictions. For example, the headline incongruence prediction  122  for the electronic document is a function of the paragraph-specific headline incongruence predictions. For example, the function may be an average, a weighted average, a maximum, a mode, a median, or other function of the paragraph-specific headline incongruence predictions. 
     At block  230 , the method  200  involves transmitting, responsive to the request, the headline incongruence prediction  122  for the electronic document  120 . For example, the detection subsystem  104  transmits the headline incongruence prediction  122  (e.g. a score or label) to the electronic document  120  to the system from which the request to determine the headline incongruence prediction  122  was received. In some embodiments, the detection subsystem  104 , or the system to which the headline incongruence prediction  122  is transmitted, modifies features of an online computing environment based on the prediction of headline incongruence by the headline incongruence detection model  109  for the electronic document  120 . 
     In some instances, modifying the features of the online computing environment include deleting, limiting access to, labeling, or otherwise performing an action with respect to the electronic document  120  associated with the headline incongruence prediction  122 . For example, the detection subsystem  104  or the system to which the headline incongruence prediction  122  is transmitted compares the headline incongruence prediction  122  against a threshold value, if the headline incongruence prediction  122  is greater than the threshold value, the, the detection subsystem  104  or the system deletes or otherwise restricts access to the electronic document  120 . In some instances, labeling the document could include displaying, via a user interface of a user device in response to receiving a hover input or clicking input with respect to a link to the electronic document  120 , a warning label alerting the user that the electronic document  120  is headline-incongruent. For example, the label reads “this text of this document/article has been determined potentially to be irrelevant to the headline,” “the quality of this article is predicted to be low,” or other text that alerts the user that the electronic document  120  exhibits headline incongruence, in accordance with the headline incongruence prediction  122 . 
       FIG.  3    depicts an illustration of a headline incongruence detection model  109  for use in certain embodiments described herein, for example as described in  FIG.  1    and  FIG.  2   . As depicted in  FIG.  3   , the headline incongruence detection model  109  includes a hierarchical dual encoder  305 , an edge learning model  310 , a feature propagation model  315 , and an incongruity prediction model  320 . A process for training the headline incongruence detection model  109  is described in  FIG.  5   . In certain examples, the headline incongruence detection model  109  receives an electronic document  120 , which, in some instances, may be a news article including a news headline and P paragraphs, as depicted in  FIG.  3   . In the example depicted in  FIG.  3   , the news headline includes a text “Miracle food . . . ,” paragraph 1 of the P paragraphs starts with a text “These superfoods . . . ,” paragraph 2 of the P paragraphs starts with a text “Every . . . ,” and paragraph P of the P paragraphs starts with a text “Click . . . ” 
     The hierarchical dual encoder  305  determines, for each of the headline and the P paragraphs of the electronic document  120 , a respective hidden state, which are depicted in  FIG.  3    as h head , h 1 , h 2 , . . . h P . In some instances, the hierarchical dual encoder model  305  applies one or more recurrent neural networks (RNNs) to headline text and texts of each of the plurality of paragraphs, which are input to the RNN as word sequences, to determine the hidden states. In some embodiments, as depicted in  FIG.  3   , a Headline RNN determines a headline hidden state h head , based on the headline text of the electronic document  120  and a Paragraph RNN determines paragraph hidden states h 1 , h 2 , . . . h P . for each of the P paragraphs of the electronic document  120 . In some embodiments, the Headline RNN includes a gated-recurrent-unit-based (GRU-based) bidirectional RNN to which word sequences for the headline is input to encode the input into a fixed-size vector. Based on the input word sequences for the headline, the GRU-based bidirectional Headline RNN outputs a respective final hidden state for the headline, as depicted in  FIG.  3   . In some embodiments, the Paragraph RNN includes a GRU-based bidirectional RNN to which world-level representations  307  (word sequences) for each of the P paragraphs are input to determine a last hidden state of the GRU-based bidirectional RNN as a paragraph-level representation  309  of each paragraph. The GRU-based bidirectional RNN then learns the paragraph representation (h 1 , h 2 , . . . h P ) from the first level of the GRU-based bidirectional RNN and the context-aware paragraph-level representations  309 . In some instances, the hierarchical dual encoder model  305  applies one or more neural networks that include sequential models capable of sequential encoding (e.g. transformer neural networks) to headline texts and to texts of each of the plurality of paragraphs, which are input to the neural networks as word sequences, to determine the hidden states. In certain embodiments, a Headline transformer neural network determines the headline hidden state h head , based on the headline text of the electronic document  120  and a Paragraph transformer neural network determines the paragraph hidden states h 1 , h 2 , . . . h P . for each of the P paragraphs of the electronic document  120 . 
     The edge learning model  310  determines a textual similarity between the headline and each of the P paragraphs of the electronic document  120 . The edge learning model determines edge weights (e 1  . . . e P ) between the headline and each of the P paragraphs, the edge weights representing a textual similarity between the headline and each of the P paragraphs. In an embodiment, each respective edge weight is determined as a function of the hidden states of the headline and the respective paragraph. In this embodiment, the function may be a bilinear operation with sigmoid nonlinearity, for example, 
         e   i =σ( h   head   T   W   E     h     i   +b   E ),  (1)
 
     where W E  and b E  weights determined through training,  h   i  is the hidden state of the respective paragraph, and h head  is the hidden state of the headline. The use of a sigmoid function bounds the edge weight to a value between zero and one. 
     The feature propagation model  315  generates a graph for the headline and the P paragraphs of the electronic document  120  based on the hidden states of the headline and the paragraphs. The graph includes a headline node associated with the headline of the electronic document  120 , P paragraph nodes associated with the P paragraphs of the electronic document  120 , and edges connecting the headline node to each paragraph node of the graph. The feature propagation model  315  assigns, to the headline node and each of the P paragraph nodes, the hidden states associated with the headline and P paragraphs as determined by the hierarchical dual encoder  305 . The feature propagation model  315  assigns, to each edge connecting the headline node to a respective paragraph node, a respective edge weight. The respective edge weight represents the textual similarity between the respective paragraph associated with the respective paragraph node and the headline associated with the headline node. In some embodiments, edges connecting paragraphs having a greater headline congruency were assigned a greater edge weight e i  than edges connecting paragraphs having a lesser headline congruency. In some embodiments, the feature propagation model  315  iteratively applies a feature propagation algorithm on the graph to propagate the node features (i.e. hidden states) into neighboring nodes of the graph according to the graph structure, which includes edges between nodes defining edge weights. In some embodiments, the feature propagation algorithm is a graph convolutional network (GCN) aggregation function, for example: 
     
       
         
           
             
               
                 
                   
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     where z i   (k)  is information propagated to the i-th node from the corresponding set of neighbor nodes  ( i ), e ij  is the edge weight, and  d   i  is the degree of the i-th node in the augmented graph including self-loops. The edge weights for self-loops e ii  are set to 1. After feature aggregation, a non-linear transformation is applied to the resulted output as follows: 
         h   i   (k+1) =ReLu( W   G   (k)   z   i   (k)   +b   G   (k)   (3)
 
     where W G   (k)  and b G   (k)  are trainable weights. The graph propagation layer is iterated for k times with residual connections. An illustration of a graph representing an electronic document  120  is depicted in  FIG.  8   . 
     The incongruity prediction model  320  determines a headline incongruence prediction  326  for each of the P paragraphs of the electronic document  120  based on the headline node features and paragraph node features of the graph as updated by the feature propagation model. The incongruity prediction model determines an overall headline incongruence prediction  122  for the electronic document  120  based on the headline incongruence predictions  326  for each of the P paragraphs of the electronic document  120 . For example, the incongruity prediction model determines the overall headline incongruence prediction  122  for the electronic document  120  as a function of the headline incongruence predictions  326  associated with the P paragraphs of the electronic document  120 . For example, the function may be a mean, a weighted mean, a mode, a median, or maximum, a minimum, an if/then or other conditional function, or other function of the headline incongruence predictions  326  associated with the P paragraphs of the electronic document  120 . In some examples, the incongruity prediction model  320  includes one or more of global-local fusion (GLF) layers (e.g. GLF layer  324 ), fully connected (FC) layers (e.g. FC layers  321 ,  322 ,  323 ), bilinear operations  325 , or other suitable neural network architecture, as described herein (e.g. as illustrated in  FIG.  3   ). For example, the node embedding outputs of the graph (e.g. the updated hidden states) are passed through an FC layer  321 . The FC layer  321  generates an output from an input (updated hidden states) as follows: x output =σ(x input W+b), where W is a trainable weight and b is a bias term, where W=   input_dim×input_dim . 
     The GLF layer  324  concatenates each node embedding with max-pooled and sum-pooled representations of the node embeddings in the graph. For example, the input to the GLF layer  324  is the headline vector h head   (k)  after the feature propagation step and FC layer  321  and paragraph vectors h 1   (k) , h 1   (k) , . . . , h |P|   (k)  after the feature propagation step and FC layer  321 . The GLF layer  324  determines an element-wise average among all the headline and paragraph vectors x mean =average(h head   (k) , h 1   (k) , h 2   (k) , . . . , h |P|   (k) ) and an element wise maximum among all the headline and paragraph vectors x max =average(h head   (k) , h 1   (k) , h 2   (k) , . . . , h |P|   (k) ). Output node embeddings of the GLF layer  324  include a concatenated headline vector, h head   new =concat(h head   (k) , x mean , x max ) and concatenated paragraph vectors:
         h 1   new =concat(h 1   (k) , x mean , x max ),   h 2   new =concat(h 2   (k) , x mean , x max ), . . . .   h |P|   new =concat(h 1   (k) , x mean , x max ,       

     The output node embeddings of the GLF layer  324  are passed through FC layers  322  and  323  to compute a headline representation v head  and paragraph representations {v 1 , . . . , v P }. The FC layer  323  generates an output from an input (output of GLF layer  324 ) as follows: x output =σ(x input W+b), where W is a trainable weight and b is a bias term, where W=   3*input_dim×input_dim . The FC layer  325  generates an output from an input (output of FC layer  322 ) as follows: x output =σ(x input W+b), where W is a trainable weight and b is a bias term, where W=   input_dim×input_dim . 
     Using a bilinear operation, the incongruity prediction model  320  determines a paragraph-specific headline incongruence prediction  326  for each paragraph i of the P paragraphs. In some embodiments, the bilinear operation receives, as input, a headline vector h head  and paragraph vectors h 1   (k) , h 2   (k) , . . . , h |P|   (k)  output by the FC layer  323  and outputs h head   T Wh 1 +b, (h head ) T Wh 2 +b, . . . , (h head ) T Wh |P| +b. From each of these outputs, a paragraph-specific headline incongruence prediction  326  is determined as follows: 
         ŷ   i =σ( v   head   T   W   B   v   i   +b   B )  (4)
 
     where ŷ i  is the paragraph-specific headline incongruence prediction  326  for paragraph i of the P paragraphs, σ is a sigmoid linear activation function, W B .and b B  are learned model parameters, v head  is the updated headline representation, and v i  is the updated representation for the paragraph i. 
     In some embodiments, the incongruity prediction model  320  determines the overall headline incongruence prediction  122  based on the determined paragraph-specific headline incongruence predictions  326 . For example, the incongruity prediction model  320  determines the overall headline incongruence prediction  122  as a function of the determined paragraph-specific headline incongruence predictions  326 . The function to determine the overall headline incongruence prediction  122  may include a mean, weighted mean, median, mode, maximum, minimum, or other function. For example, the function could be: 
         ŷ =max{ ŷ   1   , . . . ,ŷ   |P| }  (5)
 
     a maximum of a set of the paragraph-specific headline incongruence predictions  326  for the P paragraphs of the electronic document  120 . 
       FIG.  4    depicts an illustration of example headline-incongruent electronic documents, in accordance with certain embodiments described herein.  FIG.  4    depicts a headline-incongruent electronic document  401  and a headline-incongruent electronic document  402 . The headline-incongruent electronic document  401  exhibits a type of headline incongruence called partial representation. In partial representation, the headline makes claims that only partly represent a story recited in the body text. An electronic document with partial representation headline incongruence may introduce multiple stories in the body text, where the headline only describes one of the multiple stories and fails to cover the multiple stories embodied in the body text. For example, in electronic document  401 , the stories of the body text include “Extreme Weather in South Florida,” “Garden Club Event Canceled,” and “Alleged Bigfoot Sighting in Brazil,” however, the title “Extreme Weather Hits South Florida Coast” is only similar to the story “Extreme Weather in South Florida” and does not correspond to the other two stories depicted in the electronic document  401 . In certain embodiments, the incongruent headline detection system  102  can predict, by applying the headline incongruence detection model  109 , a headline incongruence prediction  122  for an electronic document  120  exhibiting partial representation. In these embodiments, the headline incongruence prediction  122  for the electronic document  120  exhibiting partial representation indicates that the electronic document  120  is headline-incongruent. 
     The headline-incongruent electronic document  402  exhibits a type of headline incongruence called incorrect representation. In incorrect representation, the headline is distinct or otherwise different from the body text of the electronic document. An electronic document with incorrect representation headline incongruence may promise to provide specific information, yet the body text does not provide the specific information promised by the headline. For example, in electronic document  402 , the headline is “Why We Should All Be Eating More Turkey,” however the body text, though the first paragraph describes why one should be eating more turkey, the rest of the body text is directed to a recipe for preparing a turkey dish, which is not related to the information promised by the headline of the electronic document  402  (i.e. arguments in favor of eating more turkey). In certain embodiments, the incongruent headline detection system  102  can predict, by applying the headline incongruence detection model  109 , a headline incongruence prediction  122  for an electronic document  120  exhibiting incorrect representation. In these embodiments, the headline incongruence prediction  122  for the electronic document  120  exhibiting incorrect representation indicates that the electronic document  120  is headline-incongruent. 
       FIG.  5    depicts an example of a process  500  for training the headline incongruence detection model  109  of  FIG.  1    (e.g. as illustrated in  FIG.  3   ) for performing the process described in  FIG.  2    of determining a headline incongruence prediction  122  for an electronic document  120 . One or more computing devices (e.g., the incongruent headline detection system  102  or the individual modules contained therein) implement operations depicted in  FIG.  5   . For illustrative purposes, the process  500  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     At block  510 , the method  500  involves generating, by the training data generator module  107 , a training data set  114 . The training data set  114  includes a set of headline-incongruent documents and headline-congruent documents. The training data generator module  107  may generate the set of headline-incongruent documents based on a set of headline-congruent documents (e.g. from a document data set  112 ). In an example, to generate a headline-incongruent document, the training data generator module  107  retrieves a first headline-congruent document. The first headline-congruent document has a headline and paragraphs. The training data generator module  107  retrieves a second headline-congruent document that has a headline and paragraphs. The training data generator module  107  replaces, in the first headline-congruent document, one or more paragraphs with one or more paragraphs from the second headline-congruent document. In this example, the headline-incongruent document includes the original headline of the first headline-congruent document, one or more paragraphs of the second headline-congruent document that replaced paragraphs of the first-headline congruent document, and remaining paragraphs of the first headline-congruent document that were not replaced with paragraphs from the second headline-congruent document. In some instances, the training data includes the set of headline-congruent documents and the generated set of headline-incongruent documents. In some instances, the training data set  114  includes the set of headline-congruent documents and the generated set of headline-incongruent documents. A method for generating the training data set  114  is described in further detail herein in  FIG.  6   . 
     At block  520 , the method  500  involves constructing, by the training module  108 , a headline incongruence model  109  including a hierarchical dual encoder  305 , an edge learning model  310 , a feature propagation model  315 , and an incongruity prediction model  320 . An example architecture of the headline incongruence model  109  is described in  FIG.  3   . 
     At block  530 , the method  500  involves the training module  108  determining, using the hierarchical dual encoder  305 , a hidden state for each of the headline and paragraphs of an electronic document  120  in the training data set  114 . The hierarchical dual encoder  305  determines, for each of the headline and the P paragraphs of the electronic document  120 , a respective hidden state. In some instances, the hierarchical dual encoder  305  applies one or more recurrent neural networks (RNNs) to headline text and texts of each of the plurality of the P paragraphs, which are input to the RNN as word sequences, to determine the hidden states. In some embodiments a Headline RNN determines a headline hidden state, based on the headline text of the electronic document  120  and a Paragraph RNN determines paragraph hidden states for each of the P paragraphs of the electronic document  120 . In some embodiments, the Headline RNN includes a gated-recurrent-unit-based (GRU-based) bidirectional RNN to which word sequences for the headline is input to encode the input into a fixed-size vector. Based on the input word sequences for the headline, the GRU-based bidirectional Headline RNN outputs a respective final hidden state for the headline. In some embodiments, the Paragraph RNN includes a GRU-based bidirectional RNN to which world-level representations  307  (word sequences) for each of the P paragraphs are input to determine a last hidden state of the GRU-based bidirectional RNN as a paragraph-level representation  309  of each paragraph. The GRU-based bidirectional RNN then learns the paragraph representation from the first level of the GRU-based bidirectional RNN and the context-aware paragraph-level representations  309 . 
     At block  540 , the method  500  involves determining, by the training module  108  using the edge learning model  310 , a textual similarity between the headline and each of the P paragraphs of the electronic document  120 . The edge learning model  310  determines edge weights between the headline and each of the P paragraphs, the edge weights representing a textual similarity between the headline and each of the P paragraphs. In an embodiment, each respective edge weight is determined as a function of the representations, as determined by the hierarchical dual node encoder  305 , of the headline and the respective paragraph. In this embodiment, the function may be a bilinear operation with sigmoid nonlinearity, for example, as described previously in Equation 1. The use of a sigmoid function bounds the edge weight to a value between zero and one. In some embodiments, edges including paragraphs having a greater headline congruency are assigned a greater edge weight than paragraphs having a lesser headline congruency. 
     At block  550 , the method  500  involves the training module  108  generating using the feature propagation model  315 , a graph for the headline and the paragraphs based on the hidden states and the textual similarities. The feature propagation model  315  generates a graph for the headline and the P paragraphs of the electronic document  120  based on the representations (hidden states) of the headline and the P paragraphs. The graph is an undirected graph G=(V, E) for the electronic document  120  that represents its innate structure, where V are nodes comprising the headline and each paragraph of the P paragraphs of the electronic document  120  and E are edges formed between the headline and each paragraph of the P paragraphs of the electronic document, resulting in a total of E=P edges. The graph includes a headline node associated with the headline of the electronic document  120 , P paragraph nodes associated with the P paragraphs of the electronic document  120 , and edges connecting the headline node to each paragraph node of the graph. The feature propagation model  315  assigns, to each edge connecting the headline node to a respective paragraph node, the edge weight determined by the edge learning model  310  that represents the textual similarity between the respective paragraph associated with the respective paragraph node and the headline associated with the headline node. 
     At block  560 , the method  500  involves updating, by the training module  108 , the graph by applying a feature propagation process on the graph. In some embodiments, the feature propagation model  315  iteratively applies a feature propagation algorithm on the graph to propagate the node features (i.e. hidden states) into neighboring nodes of the graph according to the graph structure, which includes edges between nodes defining edge weights. In some embodiments, the feature propagation algorithm is a graph convolutional network (GCN) aggregation function, for example, Equation 2 as described previously. In some embodiments, after feature aggregation, a non-linear transformation is applied to the resulted output as described in Equation 3 as described previously. The graph propagation layer is iterated for k times with residual connections. An illustration of a graph representing an electronic document  120  is depicted in  FIG.  8   . 
     At block  570 , the method  500  involves determining, by the training module  108  using the incongruity prediction model  320 , a headline incongruence prediction  326  for each of the paragraphs and an overall headline incongruence prediction  122  for the electronic document  120 . The incongruity prediction model  320  determines an overall headline incongruence prediction  122  for the electronic document  120  based on the headline incongruence predictions  326  for each of the P paragraphs of the electronic document  120 . For example, the incongruity prediction model determines the overall headline incongruence prediction  122  for the electronic document  120  as a function of the headline incongruence predictions  326  associated with the P paragraphs of the electronic document  120 . For example, the function may be a mean, a weighted mean, a mode, a median, or maximum, a minimum, an if/then or other conditional function, or other function of the headline incongruence predictions  326  associated with the P paragraphs of the electronic document  120 . 
     In some examples, the incongruity prediction model  320  includes one or more of a GLF layer, FC layers, bilinear operationsm, or other suitable neural network architecture, as described herein (e.g. as illustrated in  FIG.  3   ). For example, the node embedding outputs of the graph (e.g. the updated hidden states) are passed through a FC layer. A GLF layer concatenates each node embedding with max-pooled and sum-pooled representations of the node embeddings in the graph. Output node embeddings of the GLF layer are passed through two FC layers to compute a headline representation and paragraph representations. Using a bilinear operation, the incongruity prediction model  320  determines a paragraph-specific headline incongruence prediction  326  for each paragraph i of the P paragraphs using a bilinear operation, for example, the bilinear operation described previously in Equation 4. In some embodiments, the incongruity prediction model  320  determines the overall headline incongruence prediction  122  based on the determined paragraph-specific headline incongruence predictions  326 . For example, the incongruity prediction model  320  determines the overall headline incongruence prediction  122  as a function of the determined paragraph-specific headline incongruence predictions  326 . The function to determine the overall headline incongruence prediction  122  may include a mean, weighted mean, median, mode, maximum, minimum, or other function. For example, the function could be a maximum of a set of the paragraph-specific headline incongruence predictions  326  for the P paragraphs of the electronic document  120 , as described previously in Equation 5. 
     At block  580 , the method  500  involves determining, by the training module  108 , a loss function based on the headline incongruence predictions  326  for the paragraphs and the overall headline incongruence prediction  122 . In some embodiments, the loss function for the overall headline incongruence prediction  122  for the electronic document  120  is: 
           document   =CE ( ŷ,y )  (6)
 
     where CE is a cross-entropy loss between the overall headline incongruence prediction  122 , represented by ŷ and a ground truth headline incongruence label for the electronic document  120 , represented by y. 
     In some embodiments, the training module  108  determines an edge loss function as one of the following: 
     
       
         
           
             
               
                 
                   
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     where y is the incongruity label of the input electronic document  120 , CE is the cross-entropy loss. Using the edge loss function causes edges of headline-congruent paragraphs (e.g. those that correspond to the title based on textual similarity) of the electronic document  120  to be retained, while causing the edges of headline-incongruent paragraphs to be masked. 
     In some embodiments, the training module  108  determines a combined loss function    combined  based on the loss function for the headline incongruence prediction  122 ,    document  and the edge loss function    edge : 
           combined =   document +λ   edge   (9)
 
     where λ is a hyperparameter for adjusting a tradeoff between the component loss functions. 
     At block  590 , the method  500  involves adjusting, by the training module  108 , parameters of one or more of the models  305 ,  310 ,  315 , and/or  320  of the headline incongruence detection model  109  to minimize the loss function. For example, the training module  108  adjusts parameters to minimize the combined loss function. Example parameters include the trainable weights for determining an edge weight (Equation 1), trainable weights for the linear transformation (Equation 3) of the feature propagation step, learned model parameters of the bilinear operation (Equation 4) of the headline-incongruity prediction step, and the hyperparameter used in determining the combined loss function (Equation 8). Blocks  520 - 590  can be repeated until the loss function is minimized. 
       FIG.  6    depicts an example of a process  600  for generating a training data set  114 . Process  600  can be used implement block  510  of process  500  described in  FIG.  5   . One or more computing devices (e.g., the incongruent headline detection system  102  or the individual modules contained therein) implement operations depicted in  FIG.  6   . For illustrative purposes, the process  600  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     At block  610 , the process  600  involves retrieving, by the training data generator module  107 , a set of headline-congruent documents. The training data generator module  107  may generate a set of headline-incongruent documents based on the retrieved set of headline-congruent documents (e.g. from a document data set  112 ). In certain embodiments, the document data set  112  includes a collection of electronic documents  120  known to exhibit headline congruence. For example, the document data set  112  could be a database of news articles from one or more reputable news publishers. 
     At block  620 , the process  600  involves generating, by the training data generator module  107 , a set of headline-incongruent documents from the set of headline-congruent documents. In an example, to generate a headline-incongruent document, the training data generator module  107  retrieves a first headline-congruent document. The first headline-congruent document has a headline and paragraphs. The training data generator module  107  retrieves a second headline-congruent document that has a headline and paragraphs. The training data generator module  107  replaces, in the first headline-congruent document, one or more paragraphs with one or more paragraphs from the second headline-congruent document. In this example, the headline-incongruent document includes the original headline of the first headline-congruent document, one or more paragraphs of the second headline-congruent document that replaced paragraphs of the first-headline congruent document, and remaining paragraphs of the first headline-congruent document that were not replaced with paragraphs from the second headline-congruent document. In some instances, the training data includes the set of headline-congruent documents and the generated set of headline-incongruent documents. In some instances, the training data set  114  includes the set of headline-congruent documents and the generated set of headline-incongruent documents. In some instances, the training data includes the set of headline-congruent documents and the generated set of headline-incongruent documents. For example, the training data generator module  107  replaces a portion of a body text of a first target news article from the document data set  112  with a portion of a body text of a second target news article from the document data set  112 . 
     At block  630 , the process  600  involves generating, by the training data generator module  107 , a training data set  114  of documents from the set of headline-congruent documents and the set of headline-incongruent documents. For example, the training data generator module  107  selects unmodified electronic documents from the document data set  112  and headline-incongruent documents generated by the training data generator module  107 .  FIG.  7    depicts an illustration of generation of a training data set  114  from a document data set  112 . 
       FIG.  7    depicts an illustration of generation of headline-incongruent documents for a training data set  114  from headline-congruent documents of a document data set  112 . As depicted in  FIG.  7   , a headline-congruent document set  701  (e.g. a document data set  112 ) includes n headline-congruent electronic documents  701 - 1 ,  701 - 2 , . . . ,  701 - n . As depicted in  FIG.  7   , a headline-incongruent document set  702  including n headline-incongruent documents  702 - 1 ,  702 - 2 ,  702 - 3 ,  702 - 4 , . . . ,  702 - n  that are generated from the headline-congruent document set  701 . As depicted in  FIG.  7   , portions of two headline-congruent document  701 - 1  (which includes headline H-1 and paragraphs P1-1, P2-1, and P3-1) and 702-2 (which includes headline H-2 and paragraphs P2-1, P2-2, and P2-3) are mixed to generate headline-incongruent documents  702 - 1 ,  702 - 2 ,  702 - 3 , and  702 - 4 . 
     For example, the headline H-1 of headline-congruent document  701 - 1  is used to generate each of the headline-incongruent documents  702 - 1 ,  702 - 2 ,  702 - 3 , and  702 - 4 . In some embodiments, headline-incongruent document  702 - 1  (e.g. type A) is generated by inserting paragraphs P2-2 and P3-3 between paragraphs P2-1 and P3-1 of headline congruent document  702 - 1 . In some embodiments, headline-incongruent document  702 - 2  (e.g. type B) is generated by inserting paragraph P1-2 from headline-congruent document  701 - 2  before paragraphs P1-1, P2-1, and P3-1 of headline congruent document  701 - 1  and inserting paragraph P3-2 from the headline-congruent document  701 - 2  after paragraphs P1-1, P2-1, and P3-1. In some embodiments, headline-incongruent document  702 - 3  (e.g. type C) is generated by replacing paragraphs P1-2 and P1-3 of headline-congruent document  701 - 1  with paragraphs P2-2 and P2-3 with headline-congruent document  701 - 2  of headline-congruent document  701 - 2 . In some embodiments, headline-incongruent document  702 - 4  (e.g. type D) is generated by replacing paragraph P2-1 of headline-congruent document  701 - 1  with paragraph P2-1 of headline-congruent document  701 - 2 . In certain embodiments, the training data generator module generates n headline-incongruent documents from the n headline-congruent documents. In certain embodiments, the training data set  114  includes the n headline-incongruent documents and the n headline-congruent documents. 
       FIG.  8    depicts an illustration of generating a graph representing an electronic document for use in the processes described in  FIGS.  2 - 3  and  5    herein. As depicted in  FIG.  8   , a graph  801  is generated to represent a headline H and paragraphs P1, P2, and P3 of electronic document  401 , which was discussed previously in  FIG.  4   . As shown in graph  801 , the headline node H, representing the title of the article “Extreme Weather Hits South Florida Coast,” is connected to each of paragraph nodes P1, representing the paragraph “Extreme weather . . . ,” P2, representing the paragraph “Garden Club . . . ,” and P2, representing the paragraph “Alleged Bigfoot Sighting . . . ” 
     Examples of Computing Environments for Implementing Certain Embodiments 
     Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example,  FIG.  9    depicts an example of a computer system  900 . The depicted example of the computer system  900  includes a processor  902  communicatively coupled to one or more memory devices  904 . The processor  902  executes computer-executable program code stored in a memory device  904 , accesses information stored in the memory device  904 , or both. Examples of the processor  902  include a microprocessor, an application-specific integrated circuit (“ASIC”), a field-programmable gate array (“FPGA”), or any other suitable processing device. The processor  902  can include any number of processing devices, including a single processing device. 
     The memory device  904  includes any suitable non-transitory computer-readable medium for storing program code  906 , program data  908 , or both. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript. In various examples, the memory device  404  can be volatile memory, non-volatile memory, or a combination thereof. 
     The computer system  900  executes program code  906  that configures the processor  902  to perform one or more of the operations described herein. Examples of the program code  906  include, in various embodiments, the detection subsystem  104  and the model training subsystem  106  of  FIG.  1   , which may include any other suitable systems or subsystems that perform one or more operations described herein (e.g., one or more neural networks, encoders, attention propagation subsystem and segmentation subsystem). The program code  906  may be resident in the memory device  904  or any suitable computer-readable medium and may be executed by the processor  902  or any other suitable processor. 
     The processor  902  is an integrated circuit device that can execute the program code  906 . The program code  906  can be for executing an operating system, an application system or subsystem, or both. When executed by the processor  902 , the instructions cause the processor  902  to perform operations of the program code  906 . When being executed by the processor  902 , the instructions are stored in a system memory, possibly along with data being operated on by the instructions. The system memory can be a volatile memory storage type, such as a Random Access Memory (RAM) type. The system memory is sometimes referred to as Dynamic RAM (DRAM) though need not be implemented using a DRAM-based technology. Additionally, the system memory can be implemented using non-volatile memory types, such as flash memory. 
     In some embodiments, one or more memory devices  904  store the program data  908  that includes one or more datasets described herein. In some embodiments, one or more of data sets are stored in the same memory device (e.g., one of the memory devices  904 ). In additional or alternative embodiments, one or more of the programs, data sets, models, and functions described herein are stored in different memory devices  904  accessible via a data network. One or more buses  910  are also included in the computer system  900 . The buses  910  communicatively couple one or more components of a respective one of the computer system  900 . 
     In some embodiments, the computer system  900  also includes a network interface device  912 . The network interface device  912  includes any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks. Non-limiting examples of the network interface device  912  include an Ethernet network adapter, a modem, and/or the like. The computer system  900  is able to communicate with one or more other computing devices via a data network using the network interface device  912 . 
     The computer system  900  may also include a number of external or internal devices, an input device  914 , a presentation device  916 , or other input or output devices. For example, the computer system  900  is shown with one or more input/output (“I/O”) interfaces  918 . An I/O interface  918  can receive input from input devices or provide output to output devices. An input device  914  can include any device or group of devices suitable for receiving visual, auditory, or other suitable input that controls or affects the operations of the processor  902 . Non-limiting examples of the input device  914  include a touchscreen, a mouse, a keyboard, a microphone, a separate mobile computing device, etc. A presentation device  916  can include any device or group of devices suitable for providing visual, auditory, or other suitable sensory output. Non-limiting examples of the presentation device  916  include a touchscreen, a monitor, a speaker, a separate mobile computing device, etc. 
     Although  FIG.  9    depicts the input device  914  and the presentation device  916  as being local to the computer system  900 , other implementations are possible. For instance, in some embodiments, one or more of the input device  914  and the presentation device  916  can include a remote client-computing device that communicates with computing system  900  via the network interface device  912  using one or more data networks described herein. 
     Embodiments may comprise a computer program that embodies the functions described and illustrated herein, wherein the computer program is implemented in a computer system that comprises instructions stored in a machine-readable medium and a processor that executes the instructions. However, it should be apparent that there could be many different ways of implementing embodiments in computer programming, and the embodiments should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed embodiments based on the appended flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use embodiments. Further, those skilled in the art will appreciate that one or more aspects of embodiments described herein may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computer systems. Moreover, any reference to an act being performed by a computer should not be construed as being performed by a single computer as more than one computer may perform the act. 
     The example embodiments described herein can be used with computer hardware and software that perform the methods and processing functions described previously. The systems, methods, and procedures described herein can be embodied in a programmable computer, computer-executable software, or digital circuitry. The software can be stored on computer-readable media. For example, computer-readable media can include a floppy disk, RAM, ROM, hard disk, removable media, flash memory, memory stick, optical media, magneto-optical media, CD-ROM, etc. Digital circuitry can include integrated circuits, gate arrays, building block logic, field programmable gate arrays (FPGA), etc. 
     In some embodiments, the functionality provided by computer system  900  may be offered as cloud services by a cloud service provider. For example,  FIG.  10    depicts an example of a cloud computer system  1000  offering a service for generating a headline-incongruence prediction  122  for an electronic document  120  that can be used by a number of user subscribers using user devices  1004 A,  1004 B, and  1004 C across a data network  1006 . In the example, the service for generating a headline incongruence prediction  122  for an electronic document  120  may be offered under a Software as a Service (SaaS) model. One or more users may subscribe to the service for generating a headline incongruence prediction  122  for an electronic document  120 , and the cloud computer system  800  performs the processing to provide the service for generating a headline incongruence prediction  122  for an electronic document  120  to subscribers. The cloud computer system  1000  may include one or more remote server computers  1008 . 
     The remote server computers  1008  include any suitable non-transitory computer-readable medium for storing program code  1010  (e.g., the detection subsystem  104  and the model training subsystem  106  of  FIG.  1   ) and program data  1012 , or both, which is used by the cloud computer system  1000  for providing the cloud services. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript. In various examples, the server computers  508  can include volatile memory, non volatile memory, or a combination thereof. 
     One or more of the server computers  1008  execute the program code  1010  that configures one or more processors of the server computers  1008  to perform one or more of the operations that provide video frame segmenting services, including the ability to perform both fast and accurate video semantic segmentation using a set of temporally distributed neural networks. As depicted in the embodiment in  FIG.  10   , the one or more servers providing the services to generate a headline incongruence prediction  122  for an electronic document  120  may implement the detection subsystem  104  and the model training subsystem  106 . Any other suitable systems or subsystems that perform one or more operations described herein (e.g., one or more development systems for configuring an interactive user interface) can also be implemented by the cloud computer system  1000 . 
     In certain embodiments, the cloud computer system  1000  may implement the services by executing program code and/or using program data  1012 , which may be resident in a memory device of the server computers  1008  or any suitable computer-readable medium and may be executed by the processors of the server computers  1008  or any other suitable processor. 
     In some embodiments, the program data  1012  includes one or more datasets and models described herein. Examples of these datasets include segmented video frames. In some embodiments, one or more of data sets, models, and functions are stored in the same memory device. In additional or alternative embodiments, one or more of the programs, data sets, models, and functions described herein are stored in different memory devices accessible via the data network  1006 . 
     The cloud computer system  1000  also includes a network interface device  1014  that enable communications to and from cloud computer system  1000 . In certain embodiments, the network interface device  1014  includes any device or group of devices suitable for establishing a wired or wireless data connection to the data networks  1006 . Non-limiting examples of the network interface device  1014  include an Ethernet network adapter, a modem, and/or the like. The next event prediction and dynamic clustering service is able to communicate with the user devices  1004 A,  1004 B, and  1004 C via the data network  1006  using the network interface device  1014 . 
     The example systems, methods, and acts described in the embodiments presented previously are illustrative, and, in alternative embodiments, certain acts can be performed in a different order, in parallel with one another, omitted entirely, and/or combined between different example embodiments, and/or certain additional acts can be performed, without departing from the scope and spirit of various embodiments. Accordingly, such alternative embodiments are included within the scope of claimed embodiments. 
     Although specific embodiments have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects described above are not intended as required or essential elements unless explicitly stated otherwise. Modifications of, and equivalent components or acts corresponding to, the disclosed aspects of the example embodiments, in addition to those described above, can be made by a person of ordinary skill in the art, having the benefit of the present disclosure, without departing from the spirit and scope of embodiments defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures. 
     General Considerations 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multi-purpose microprocessor-based computer systems accessing stored software that programs or configures the computer system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” herein is meant as an open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Where devices, systems, components or modules are described as being configured to perform certain operations or functions, such configuration can be accomplished, for example, by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation such as by executing computer instructions or code, or processors or cores programmed to execute code or instructions stored on a non-transitory memory medium, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for inter-process communications, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times. 
     Additionally, the use of “based on” is meant to be open and inclusive, in that, a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude the inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.