Identification of a breakpoint based on a correlation measurement

Examples herein disclose obtaining regions of digital content and determining a correlation measurement between the multiple regions of digital content adjacently located to each other. The examples disclose identifying a breakpoint in the digital content based on the determined correlation measurement.

BACKGROUND

Electronic publishing involves a digital publication of electronic books, digital magazines, and the development of digital libraries. These digital publications may be created by authors seeking to convey particular ideas. Additionally, these digital publications may be used as learning tools for a student to engage in the exchange of information and/or ideas.

DETAILED DESCRIPTION

In digital publishing, an author or teacher may curate content to create an electronic book or other type of document. In this situation during the creation of the electronic book, the author may collect the material or content and organize this material in a sequence in which the author desires; however, the author may have difficulty deciding in how to segment this material or content in such a manner that a reader may logically follow and understand key points. For example, a creator of the content may have difficulty in determining at which points in the material to create stopping and starting points through chapters and/or sections. Additionally, the author may manually decide how to segment this material to create chapters in the electronic book, thus taking much time and effort. Manually segmenting the chapters may also providing difficulties in providing coherency to the reader to ensure understanding of the material. Although the term chapter is used throughout this document as a main division of content, implementations should not be limited as chapter may additionally represent a section or other type of division of the content. As such, the terms chapter and section may be used interchangeably throughout this document.

To address these issues, examples disclosed herein provide a mechanism for automating chapter identification from content and/or material collected by an author. The examples obtain multiple regions of digital content in a sequence as collected by the author. Using this sequence, the examples calculate a correlation value between those multiple regions of digital content which are located adjacent to each other as put forth by the sequence. Using this correlation value, the examples identify a breakpoint between the multiple regions of the digital content while maintaining the sequence of digital content collected by the author. The breakpoint provides an interruption between the multiple regions of digital content, thus creating a division in the digital content which automates the identification of chapters. Automating the identification of the chapters within the digital content provides a recommendation to the author how to segment the digital content in a book or document. Identifying how to segment the digital content saves much time and effort for the author.

Additionally identifying how to segment the digital content, the examples provide a more coherent segmentation for a reader to logically understand and comprehend the content. Further automating the segmentation of the digital content into chapters provides logical units of content which may be returned in a search query. This gives the content more context by returning the logical unit (e.g., chapter) most relevant to the search request rather than returning the full document which may overwhelm the requester.

Other examples discussed herein, calculates an adjusted correlation value based upon the identification of the breakpoint. The adjusted correlation value is calculated for those regions adjacent to the identified breakpoint. Based on this adjusted correlation value, each region of content may be accurately accessed to determine which chapter is more appropriate for including that region. For example, the adjusted correlation value may determine whether a particular region should be merged into an adjacent chapter. Merging the particular region provides an adjustment or modification to the identified breakpoint to ensure each chapter includes the most appropriate content. In this implementation, the content flow is adjusted or modified, thus modifying the breakpoint so the interruption in the content may occur earlier or later in the content flow.

Yet, other examples discussed herein determine a total amount of content within the digital content and a minimum amount of content which should be included in each chapter. Accordingly, if the proposed chapter based on the identified breakpoint is below the minimum amount of content, the proposed chapter may be merged or combined into an adjacent chapter. Merging the chapters avoids excessively small chapters, thus providing additional coherence of the reader.

Thus, examples disclosed herein automate chapter identification in a book under creation. The examples calculate a correlation value for those regions of content or material which are located adjacent to one another. Calculating the correlation value, the examples may identify breakpoints between those regions of content or material. Identifying the breakpoints, the examples create the divisional segmentation between the regions of content and/or material, thus creating the chapters within the digital content.

Referring now to the figures,FIG. 1is a block diagram of an example system102including a processing engine108to obtain multiple regions104of digital content106. The processing engine108processes the multiple regions104for a correlation engine110. The correlation engine110determines a correlation value for those multiple regions104which are located adjacent to one another. Upon determining the correlation value, an identification engine112identifies a breakpoint114to insert between the multiple regions104. Upon identifying the breakpoint114, an adjusted engine116may determine an adjusted correlation value for each of the multiple regions104located adjacent to the identified breakpoint114. Thus, depending on the adjusted correlation value, the adjusted engine116may produce an adjusted breakpoint118. Although the system102depicts the engine components108,110,112, and116as separate components from one another, this was done for illustration purposes as these engine components108,110,112, and116may include a combination of such components108,110,112, and116.

The multiple regions104are those blocks of content which constitute the digital content106. The digital content106is considered that content which may constitute a document or electronic book in which a user may wish to insert at least one breakpoint. The breakpoint provides an interruption between the multiple regions104in accordance with the calculated correlation value. As such, the breakpoint represents the interruption in which to divide the digital content106to create multiple chapters. The multiple regions104are subsections of the digital content106in which the breakpoint is inserted between these regions104to create the chapters within the digital content106. As such, the multiple regions104may include by way of example, articles pages, paragraphs, sentences, etc. In one implementation, the user creates the multiple regions104of the digital content106. In another implementation, the user creates a full document of the digital content106. In this implementation, the system102obtains the digital content106and segments the content into the multiple regions104.

The processing engine108obtains the multiple regions104for processing the content within each of the multiple regions104. The processing engine108may include processing each of the multiple regions104for the removal of stop text and/or stemming text. Stop text are those words or text which are filtered out before and after processing of the content within the multiple regions104. Examples of stop words may include but should not be limited to the, is, at, which, on, etc. Stemming text are those words which are reduced to the word's root form which may occur before and after processing the content within the multiple regions104. The processing engine108may include, electronic circuitry (i.e., hardware) that implements the functionality of the processing engine108. In this example, processing engine108may include by way of example, an integrated circuit, application integrated circuit (ASIC), controller, processor, semiconductor, processing resource, chipset, semiconductor, or other type of hardware component capable of the functionality of the processing engine108. Alternatively, the processing engine108may include instructions (e.g., stored on a machine-readable medium) that, when executed by a hardware component (e.g., processor and/or controller), implement the functionality of the processing engine108.

The correlation engine110calculates the correlation value for each pair of multiple regions104which are located adjacent to one another. For example, the pair of multiple regions104which are located adjacent to one another may include: Region1and Region2; Region2and Region3; Region N−1 and Region N, etc. The correlation value is a measurement of the related content and/or similar content between these pairs of adjacent regions104. The correlation value may be calculated using various mechanisms including, by way of example, Pearson correlation, topic model, BM25, or other type of correlation metrics. The correlation value is explained in detail in later figures. The correlation engine110may be similar in functionality to the processing engine108. As such, implementations of the correlation engine110may include electronic circuitry (i.e., hardware) that implements the functionality of the correlation engine110. Alternatively, the correlation engine110may include instructions (e.g., stored on a machine-readable medium) that, when executed by the hardware component (e.g., processor and/or controller), implement the functionality of the correlation engine110.

The identification engine112uses the correlation value produced by the correlation engine110to identify the breakpoint114between the multiple regions104. In one implementation, the identification engine112determines which correlation values are the lower values among multiple correlation values. In this implementation, it is assumed that the correlation value for the pair for adjacent multiple regions104which belong in different chapters would be lower than the pair of adjacent multiple regions104which belong in the same chapter. The lower value indicates that the content located in that pair of adjacent multiple regions104are less related and/or less similar than other pairs of adjacent multiple regions104which have a higher calculated correlation value. In another implementation, if the correlation value is below a particular threshold, this may indicate to insert the breakpoint between those regions104. The identification engine112may be similar in functionality to the processing engine108. As such, implementations of the identification engine112may include electronic circuitry (i.e., hardware) that implements the functionality of the identification engine112. Alternatively, the identification engine112may include instructions (e.g., stored on a machine-readable medium) that, when executed by the hardware component (e.g., processor and/or controller), implement the functionality of the identification engine112.

The identified breakpoint114is an interruption between the multiple regions104that creates the chapters within the digital content106. As such, the identified breakpoint114provides a stopping point from one chapter prior to an adjacent chapter. The identified breakpoint114is based on the calculated correlation value calculated at engine110which enables the system102to automate the identification of chapters within the digital content106. In one implementation, the identified breakpoint114serves as an initial or proposed breakpoint. In this implementation, the adjustment engine116calculates the adjusted correlation value based on the location of each region104adjacent to the initial breakpoint. This adjusted correlation value enables the adjustment engine116to identify whether a particular region should be merged into an adjacent chapter or remain in the chapter in which it belongs. In this implementation, if the particular region is excluded and/or merged into one of the chapters, the adjustment engine116may further adjust the initial breakpoint.

The adjustment engine116calculates the adjusted correlation value based on the identified breakpoint114. The adjusted correlation value is that value for the region104which is located adjacent to the identified breakpoint114. The adjusted correlation value is discussed in detail in a later figure. The adjustment engine116may be similar in functionality to the processing engine108. As such, implementations of the adjustment engine116may include electronic circuitry (i.e., hardware) that implements the functionality of the adjustment engine116. Alternatively, the adjustment engine116may include instructions (e.g., stored on a machine-readable medium) that, when executed by the hardware component (e.g., processor and/or controller), implement the functionality of the adjustment engine116.

The adjusted breakpoint118is a modification to the identified breakpoint114based on the adjusted correlation value as calculated by the adjustment engine116. In one implementation, the adjustment engine116may determine a total length of the digital content106and a minimum amount of content which should be included in each chapter. Thus, considering each initial breakpoint, the adjustment engine116may determine if each chapter meets the minimum amount of content. If the chapter falls below the minimum amount of content, that chapter may be merged into an adjacent chapter, thus also adjusting the initial breakpoint to include the merged chapter. These implementations are discussed in details in a later figure.

FIG. 2Ais a block diagram illustrating multiple breakpoints206in which to divide digital content composed of multiple regions204of content into multiple chapters208. The multiple regions204between the breakpoints206compose each respective chapter208. For example, the first chapter208(Chapter1) includes regions of content204from Region1to Region(i−1).

As illustrated inFIG. 2A, to create at least three chapters208(Chapter1, Chapter2, and Chapter3), two breakpoints206are identified for breaking up the multiple regions204of content. The breakpoints206are identified based on a correlation value between those pairs of multiple regions204which are located adjacently to one another. The correlation value is calculated to determine how related the content in each of the multiple regions204are to one another. This correlation value is calculated prior to identifying the various breakpoints206. Rather, the correlation value is used to identify these breakpoints206. For example the pairs of adjacent multiple regions204may include the following: Region1and Region2; Region2and Region3; Regioniand Region(i+1); Regionjand Region(j+1); and Region(n−1)and Regionn. The correlation value may be calculated using various mechanisms including, by way of example, Pearson correlation, topic model, BM25, or other type of correlation metrics. In another implementation, a sequence of the multiple regions204is determined based upon the order a user may input the digital content. In this implementation, the order the digital content is uploaded is the order or sequence of the multiple regions204. In this implementation, the order of the multiple regions204is set according to the order in which the digital content is input.

FIG. 2Bis a graph illustrating an identification of multiple breakpoints206between multiple regions of digital content. The multiple breakpoints206are identified based on a correlation value210. The correlation value210as observed on the x-axis of the graph, is a value which is produced based on relatedness of content between a pair of adjacent multiple regions. The chapters208are observed on the y-axis of the graph corresponding to the positions of the multiple regions204.

As illustrated inFIG. 2B, it is assumed that the correlation value of a pair of adjacent multiple regions204which belong to different chapters208are lower than a pair of adjacent multiple regions204which belong in the same chapter208. In one implementation, the breakpoints206are identified among those correlation values210which are lower values. For example, the first breakpoint206(BreakPoint1), is a lower correlation value210between Regioniand Region(i+1)than the correlation value corresponding to Region1and Region2. The lower the correlation value210indicates the content within those regions are less related than other regions. In one implementation, these breakpoints206are considered the initial breakpoints in that the breakpoints206may be modified to include or exclude particular regions of content. In this implementation, an adjusted correlation value is calculated for each of the multiple regions204which are located adjacent to the initial breakpoints206. Based on this adjusted correlation value, the initial breakpoints206may be adjusted to include or exclude the particular regions of content. This implementation is explained in detail later figures.

FIG. 3Ais a diagram of example breakpoints306illustrating chapters308among multiple regions304of digital content. As illustrated inFIG. 3A, the first breakpoint306(Breakpoint1) is located between a previous region of content and region304(RegionP). The second breakpoint306(Breakpoint2) is located between the multiple regions304(Regioni) and (Region(i+1)). The third breakpoint306(Breakpoint3) is located between the region304(Region(q+1)) and subsequent multiple regions. The multiple regions304between the first breakpoint306and the second breakpoint306make up the content included in the chapter308(Chapter A). The multiple regions304between the second breakpoint306and the third breakpoint306make up the content included in the chapter308(Chapter B).

Each of the breakpoints306were selected as the correlation value between the respective regions304were lower in value and thus indicates the content between those adjacent regions are not as related. The lower amount of the correlation value enables identification of those breakpoints306for the automation of the various chapters308.

In other implementations, the correlation value may be adjusted based on each of the multiple regions304adjacent to each breakpoint306. For example, these multiple regions adjacent to the breakpoints306may include RegionP, Regioni, Region(i+1), and Region(q+1). In a further example, consider the chapter308(Chapter A) which includes Regioniand the adjacent chapter308(Chapter B) which includes Region(i+1). In this example, it would be expected that the content within the region304(Regioni) is more related to the content within the chapter308(Chapter A) than the adjacent or neighboring chapter308(Chapter B). It may be possible that the content within the region304(Region) is more related to the content in the adjacent chapter308(Chapter B) than the content in the chapter308(Chapter A) in which the region304(Regioni) is situated. In this situation, an adjusted correlation value is determined as in connection withFIG. 3Bto verify which chapter308(Chapter A or Chapter B), the region304(Regioni) is more closely related.

FIG. 3Bis a diagram of a visualization of similarity between a region304(Regioni) and a central mean value of a chapter308in which the region304belongs to (Chapter A) and an adjacent chapter (Chapter B).FIG. 3Brepresents the situation where the content within the region304is more closely related to the adjacent chapter308(Chapter B) than the chapter in which it is situated (Chapter A). Thus, in this situation, the second breakpoint306as inFIG. 3Awould be adjusted or modified.

Moving the region304closer to the adjacent chapter308(Chapter B), means the similarity of that region304to the adjacent chapter308could increase or decrease accordingly. Additionally, moving the region304into the adjacent chapter308means the adjacent chapter308gains an additional region304while the original chapter (Chapter A) loses this region304of content. As such, equation 1 may be used to measure the function change of similarity between the region304and the chapters308. In equation 1, Coldis the chapter in which the region304was originally part of (Chapter A) and Cnewis the chapter (Chapter B) in which to move region304.
Δf1=sim(Ri, Cnew)−sim(Ri, Cold)  Equation (1)

The following equation 2 is used to measure a change of the correlation of the moved region304and the region adjacent to the second breaking point306. In equation 2, Rxis the adjacent region of content to the region304(Ri) across the initial second breakpoint306prior to the combination of the region304into the adjacent chapter308(Chapter B) and Ryis the adjacent region of content to the region304across the adjusted second breakpoint306after the adjustment of the region304into the adjacent chapter308(Chapter B).
Δf2=corr(Ri, Ry)−corr(Ri, Rx)  Equation (2)

The results from these equations are combined with different weights θ1and θ2wherein Δf is the overall benefit as in equation 3.
Δf=(θ1Δf1)−(θ2Δf2)  Equation (3)

InFIG. 3B, each of the multiple regions304located near each of the breaking points306determine the adjusted correlation value for selecting whether to move the multiple region304into the adjacent chapter. For each of the multiple regions304located near each of the breaking points306, it is verified whether to combine that region304into the adjacent chapter based on the overall benefit. Combining that region304into the adjacent chapter modifies adjacent breakpoint306accordingly.

FIG. 4is a flowchart of an example method, executable by a computing device, to identify a breakpoint based on a correlation measurement. The computing device obtains multiple regions of content and proceeds to determine the correlation measurement. The correlation measurement is a value which represents the relatedness between regions located adjacent to one another. The computing device uses the correlation measurement to identify the breakpoint in the digital content. The breakpoint represents a place of interruption in the digital content thus creating a division in the digital content. Identifying the breakpoint(s) within the digital content provides a mechanism in which to automate chapters) within the digital content. In discussingFIG. 4, references may be made to the components inFIGS. 1-3Bto provide contextual examples. In one implementation ofFIG. 4, the system102as inFIG. 1operates on the computing device to perform operations402-406. In this implementation, the operations402-406may operate in the background of the computing device to identify the breakpoint in the digital content. Further, althoughFIG. 4is described as implemented by the computing device, it may be executed on other suitable components. For example,FIG. 4may be implemented by a controller (not illustrated) ands or in combination with the executable instructions on a machine-readable storage medium704as inFIG. 7.

At operation402, the computing device obtains multiple regions of digital content. At operation402, a user inputs the digital content for the computing device to automate chapter(s) within the digital content. The computing device obtains the digital content when a user, such as an author, uploads content or scans the content which may be convened into a machine-readable language for processing. For example, the digital content may include a book under creation composed of multiple pieces of digital content, each piece of content may be considered a region of digital content, such as a page, paragraph, etc. Accordingly, the author generates these pieces of content and links the pieces of content together for input to create the book. In turn, the computing device may obtain this content which may have been previously segmented into multiple regions of digital content or the computing device segments the digital content into the multiple regions. Each of the regions may be semi-related in content, thus the computing device measures which regions of content may be more related than other regions of content. As such, the computing device determines the correlation measurement of the regions of content which are sequentially located adjacent to each other. In another implementation upon uploading the digital content, the user may input a number of breakpoints in which the user wishes to divide the digital content. Segmenting the digital content into the multiple regions, enables the computing device to more accurately identify the breakpoint(s) for segmenting the digital content into chapter(s) as opposed to using a full document of digital content.

At operation404, the computing device determines the correlation measurement between a pair of multiple regions which are located adjacent to one another. The multiple regions are obtained in the order according to how these regions were input as at operation402. The correlation measurement is a value obtained of relatedness between regions which are located adjacently to each other. The locations of the regions are sequenced according to how the user inputs the content. For example, the user may create a book about a zoo and as such upload pieces of content in a sequential order such as monkey, zoo, bears, fish, etc. Thus, the correlation measurement may be obtained between: monkey and zoo; zoo and bears; bears and fish. In implementations, the correlation measurement is obtained by means of a correlation function including, by way of example, a Pearson correlation, topic, model, BM25etc.

At operation406, the computing device identifies the breakpoint in the digital content based on the correlation measurement at operation404. The breakpoint is located between multiple regions of the digital content. The breakpoint breaks the digital content into multiple chapters, thus the regions of digital content may be grouped together to create the various chapters within the digital content. The order the multiple regions grouped together is maintained according to the order of the digital content is input. In one implementation, if the correlation measurement is below a particular threshold indicates there should be a break within the digital content. In another implementation, the computing device determines multiple correlation measurement values, each correlation measurement corresponds to a different pair of multiple regions located adjacent to each other. In this implementation, the correlation measurement values are ranked according to the lower ranked values. The lower ranked values indicate less relatedness between the regions and thus the breakpoint. In another implementation, the breakpoint serves as initial breakpoint which may be adjusted. This implementation is discussed in detail in later figures.

FIG. 5is a flowchart of an example method, executable by a computing device, to modify an identified breakpoint in digital content based on an adjusted correlation measurement. The computing device obtains digital content and segments the content into multiple regions. A correlation value is determined for each pair of multiple regions located adjacently to each other. These correlation values are ranked to determine which correlation values indicate there should be a breakpoint in the digital content. Ranking the correlation values, the computing device identifies the initial breakpoint in the digital content. Upon identifying the initial breakpoint, the computing device obtains a different correlation value for each of the multiple regions located adjacent to the initial breakpoint. The computing device may proceed to modify the initial breakpoint based on the different or adjusted correlation values. In discussingFIG. 5, references may be made to the components inFIGS. 1-3Bto provide contextual examples. In one implementation ofFIG. 5, the system102as inFIG. 1operates on the computing device to perform operations502-518. In this implementation, the operations502-518may operate in the background of the computing device to identify the breakpoint in the digital content. Further, althoughFIG. 5is described as implemented by the computing device, it may be executed on other suitable components. For example,FIG. 5may be implemented by a controller (not illustrated) and/or in combination with the executable instructions on a machine-readable storage medium704as inFIG. 7.

At operation502, the computing device obtains the multiple regions of digital content. In one implementation, a user inputs a document of digital content which the computing device segments into the multiple regions as at operation504. Operation502may be similar in functionality to operation402as inFIG. 4.

At operation504, the computing device segments the digital content into the multiple regions. Upon obtaining the digital content at operation502, the computing device may segment this content into regions or sections. At operation504, the digital content is broken into subsections or regions of the digital content for processing. Segmenting the digital content into the multiple regions enables the computing device to determine the correlation measurement between those pairs of regions which are adjacent to one another at operations506-510.

At operation506, the computing device determines the correlation measurement between those pairs of multiple regions which are adjacent to each other. The correlation measurement is used to determine how related the adjacent multiple regions are to one another. The more related indicates to the computing device those multiple regions should be located within the same chapter. The less related indicates to the computing device to insert a breakpoint between those multiple regions, thus creating the chapters. In one implementation, the computing device proceeds to operations508-510to determine the correlation measurement. Operation506may be similar in functionality to operation404as inFIG. 4.

At operation508, the computing device determines the correlation value for each pair of multiple regions located adjacent to one another. Each correlation value corresponds to a different pair of multiple regions, thus producing multiple correlation values the computing device may proceed to rank at operation510.

At operation510, the computing device ranks each of the correlation values obtained for each pair of multiple regions adjacently located by each other. In one implementation, the correlation values are ranked in accordance to those values which are below a particular threshold. In another implementation, the correlation values are ranked in ascending to descending order. In these implementations, the lower the amount of the correlation value indicates the less related the content is between the adjacent regions. This lesser relatedness between the adjacent regions of content indicates to the computing device there should be a breakpoint between those adjacent regions of content.

At operation512, the computing device identifies the breakpoint in the digital content. In one implementation, the computing device identifies a number of breakpoints according to a predefined number of chapters and/or a predefined number of breakpoints. In this implementation, the number of chapters and/or the number of breakpoints may be user-defined. Additionally, using the correlation values indicates the measure of how related the pair of multiple regions are to one another enabling the computing device to identify the breakpoints. Operation512may be similar in functionality to operation406as inFIG. 4.

At operation514, the computing device maintains the sequence of the multiple regions in accordance with the order or sequence in which the digital content was uploaded.

At operation516, the computing device determines the adjusted correlation measurement upon the identification of the breakpoint at operation512. The adjusted correlation measurement is a value of relatedness for each multiple region adjacent to the identified breakpoint. The value of relatedness indicates whether to combine the region to the adjacent chapter or section or to leave the multiple region as part of the initial chapter. Based on this value, the computing device may modify the breakpoint at operation518.

At operation518, the computing device modifies the identified breakpoint based on the adjusted correlation measurement at operation516. In this implementation, if the adjusted correlation measurement indicates a higher relatedness between the content region and the content adjacent chapter, the region is merged into the adjacent chapter as part of that chapter. Merging or combining the particular region into the adjacent chapter adjusts the adjacent chapter to include that particular region. This modifies the identified breakpoint to increase the content in that adjacent chapter.

FIG. 6is a flowchart of a method, executable by a computing device, to adjust a breakpoint if a length of content is below a determined minimum length of content. The computing device obtains multiple regions of digital content and determines a correlation value between those pairs of multiple regions which are located adjacent to one another. Upon determining the correlation value for the adjacent multiple regions, the computing device identifies the breakpoint as an initial breakpoint within the digital content. The breakpoint is considered an interruption between the regions of content so the most relevant content is grouped together within a chapter and/or section. As illustrated inFIG. 6, the computing device may proceed to determine a total length of digital content and thus may determine a minimum length of content which should be included in the chapter. Upon determining the minimum length, the computing device may identify whether the chapter created by the identified breakpoint is below the minimum length. If the computing device determines the chapter is below the minimum length of content, the computing device may proceed to combine the chapter in the adjacent chapter. Combining these chapters adjusts or modifies the breakpoint so the interruption in the digital may occur earlier or later. In discussingFIG. 6, references may be made to the components inFIGS. 1-3Bto provide contextual examples. In one implementation ofFIG. 6, the system102as inFIG. 1operates on the computing device to perform operations602-616. In this implementation, the operations602-616may operate in the background of the computing device to identify the breakpoint in the digital content. Further, althoughFIG. 6is described as implemented by the computing device, it may be executed on other suitable components. For example,FIG. 6may be implemented by a controller (not illustrated) and/or in combination with the executable instructions on a machine-readable storage medium704as inFIG. 7.

At operation602, the computing device may obtain the multiple regions of digital content. Operation602may be similar in functionality to operations402and502as inFIGS. 4-5.

At operation604, the computing device determines the correlation measurement between the pair(s) of those multiple regions which are located adjacent to one another in the digital content. Operation604may be similar in functionality to operations404and506as inFIGS. 4-5.

At operation606, the computing device identifies the initial breakpoint in the digital content. Operation606may be similar in functionality to operations406and512as inFIGS. 4-5.

At operation608, the computing device determines the total length of the digital content which was obtained at operation602. The total length of the digital content is used to determine the minimum length of content in each chapter.

At operation610, the computing device determines the minimum length of content which should be included in each chapter. Determining the minimum length ensures the chapters are not excessively small in content.

At operation612, the computing device determines the actual length or amount of content included in the chapter which is adjacent to the initial breakpoint. If the length of content is below the minimum amount of content, the computing device combines the chapter into the adjacent chapter thus adjusting the breakpoint at operation616. If the computing device determines the length of the chapter is not below the minimum amount of content, the computing device proceeds to operation614and does not adjust the breakpoint.

At operation614, upon the determination the actual length of the content in the chapter is not below the minimum length identified at operation610, the computing device does not adjust the breakpoint. Alternatively, upon the determination the actual length of content is equal to or above the minimum content length, the computing device may keep the initial breakpoint identified at operation606as the breakpoint for separating the digital content.

At operation616, upon the determination the chapter is below the minimum length of content, the computing device may adjust the breakpoint to combine that chapter into the neighboring chapter. Merging the chapter into the adjacent chapter also modifies the breakpoint so the breakpoint comes earlier or later in the digital content.

FIG. 7is a block diagram of computing device700with a processor702to execute instructions706-724within a machine-readable storage medium704. Specifically, the computing device700with the processor702modifies an identified breakpoint based on a correlation value and a similarity value. Although the computing device700includes processor702and machine-readable storage medium704, it may also include other components that would be suitable to one skilled in the art. For example, the computing device700may include an engine102inFIG. 1. The computing device700is an electronic device with the processor702capable of executing instructions706-724, and as such embodiments of the computing device700include a computing device, mobile device, client device, personal computer, desktop computer, laptop, tablet, video game console, or other type of electronic device capable of executing instructions706-724. The instructions706-724may be implemented as methods, functions, operations, and other processes implemented as machine-readable instructions stored on the storage medium704, which may be non-transitory, such as hardware storage devices (e.g., random access memory (RAM), read only memory (ROM), erasable programmable ROM, electrically erasable ROM, hard drives, and flash memory).

The processor702may fetch, decode, and execute instructions706-724to modify the identified breakpoints based on the correlation value and the similarity value. In one implementation, upon executing instructions706-710, the processor702may execute instruction712through execution of instruction714. In another implementation, upon executing instructions706-714, the processor702may proceed to execute instructions716-722prior to execution of instruction724. Specifically, the processor702executes instructions706-714to: receive digital content in a sequence, such as uploading and/or inputting the digital content; segment the digital content into multiple regions; determine a correlation value for a pair of multiple regions located adjacent to each other; identify an initial breakpoint based on the correlation value; and rank the correlation value among multiple correlation values, each correlation value corresponds to a different pair of multiple regions located in an adjacent manner to one another. The processor702may execute proceed to execute instructions716-724to: determine an adjusted correlation value for each of the multiple regions adjacent to the breakpoint; determine a similarity value by obtaining a central mean value of the digital content included in a chapter or section and determining if a distance of the region neighboring the breakpoint is closer to the central mean value in the chapter in which it belongs or a neighboring chapter; and modify the identified breakpoint based on the adjusted correlation value and the similarity value.

The machine-readable storage medium704includes instructions706-724for the processor702to fetch, decode, and execute. In another embodiment, the machine-readable storage medium704may be an electronic, magnetic, optical, memory, storage, flash-drive, or other physical device that contains or stores executable instructions. Thus, the machine-readable storage medium704may include, for example, Random Access Memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage drive, a memory cache, network storage, a Compact Disc Read Only Memory (CDROM) and the like. As such, the machine-readable storage medium704may include an application and/or firmware which can be utilized independently and/or in conjunction with the processor702to fetch, decode, and/or execute instructions of the machine-readable storage medium704. The application and/or firmware may be stored on the machine-readable storage medium704and/or stored on another location of the computing device700.