Video aggregation method and device, electronic device and medium

Abstract

The present disclosure provides a video aggregation method, device, electronic device, computer-readable storage medium, and computer program product, which relate to the field of artificial intelligence, and in particular to the field of deep learning and natural language processing technology. The implementation scheme is as follows: obtaining multiple videos to be aggregated; determining the video information corresponding to each of the multiple videos, the video information including at least one of a video title, an image including text information, and a description text; performing text recognition on the video information to determine one or more corresponding video tags; for at least one video tag, determining the first video collection corresponding to each video tag in the at least one video tag, each video in the first video collection corresponds to the corresponding video tag; in response to determining that the video content in at least two first video collections is the same, performing a deduplication operation; and determining the collection title of the video collection remaining after the deduplication operation.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, in particular to the fields of deep learning and natural language processing technology, and specifically to a video aggregation method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

[0003] Video collections are an important product format, significantly improving user experience. For example, when a user searches for watch-related videos, the displayed collection of watch videos offers greater user satisfaction and a superior user experience compared to a single, standard watch video. Therefore, the key is to aggregate videos within the search library into collections to supplement the library with a large collection of video collections. Summary of the Invention

[0004] The present disclosure provides a video aggregation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of the present disclosure, a video aggregation method is provided, comprising: obtaining a plurality of videos to be aggregated; determining video information corresponding to each of the plurality of videos, wherein the video information comprises at least one of a video title, an image, and a description text, wherein the image comprises text information; performing text recognition on the video information to determine one or more video tags corresponding to each of the videos; for at least one video tag among a plurality of video tags corresponding to the plurality of videos, determining a first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag; in response to determining that the video contents in at least two first video collections are identical, performing a deduplication operation on the at least two first video collections; and determining the collection titles of the one or more video collections remaining after the deduplication operation.

[0006] According to another aspect of the present disclosure, a video aggregation device is provided, comprising: an acquisition unit configured to acquire multiple videos to be aggregated; a first determination unit configured to determine video information corresponding to each of the multiple videos, wherein the video information includes at least one of a video title, an image, and a description text, wherein the image includes text information; a second determination unit configured to perform text recognition on the video information to determine one or more video tags corresponding to each of the videos; a third determination unit configured to determine, for at least one video tag among a plurality of video tags corresponding to the multiple videos, a first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag; a first deduplication unit configured to perform a deduplication operation on the at least two first video collections in response to determining that the video contents in at least two first video collections are the same; and a fourth determination unit configured to determine the collection titles of the one or more video collections remaining after the deduplication operation.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method described in the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the method described in the present disclosure when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, a large number of video collections can be generated, a large number of collection resources can be added to the search library, the amount and display of collection resources searched by users can be increased in search scenarios, and the user's search satisfaction and consumption step can be improved.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0013] Figure 1 A schematic diagram illustrating an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 A flowchart of a video aggregation method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A flowchart of performing text recognition on video information to determine video tags according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A flowchart of determining a maximum common subsequence according to an embodiment of the present disclosure is shown;

[0017] Figure 5 A flowchart of training a neural network model according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A structural block diagram of a video aggregation device according to an embodiment of the present disclosure is shown; and

[0019] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0022] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0023] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0025] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the video aggregation method to be performed.

[0026] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0027] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0028] The user can use the client device 101, 102, 103, 104, 105 and / or 106 to upload a video or input an operation instruction. The client device can provide an interface that enables the user of the client device to interact with the client device. The client device can also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0029] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0030] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0031] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0032] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0033] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0034] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0035] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0036] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0037] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0038] Video collection is an important product form that has obvious benefits for user experience. For example, when a user searches for a watch, the watch collection displayed has a good satisfaction gain and excellent user experience compared to an ordinary single watch video. Usually, a video publishing platform has its own content ecology. It relies on the author side to guide users when they publish videos, and gives a higher weight to collection resources during distribution, so as to cultivate the habit of users to publish collection resources, thereby increasing the number of collections. However, for video platforms where most of their video resources come from external sites, it is impossible to aggregate videos through author-side guidance. Therefore, there is an urgent need for a video collection generation algorithm to aggregate videos in the search library into collections, so as to supplement the search library with a large number of collection resources, increase the amount and display of collection resources searched by users in search scenarios, and improve users' search satisfaction and consumption steps.

[0039] Therefore, according to an embodiment of the present disclosure, a video aggregation method is provided. Figure 2 FIG. 2 shows a flow chart of a video aggregation method 200 according to an embodiment of the present disclosure. The method 200 may be Figure 1 The execution of each step of the method 200 may be performed by any of the client devices 101, 102, 103, 104, 105 and 106. Figure 1In some embodiments, the method 200 may be performed at the server 120. In some embodiments, the method 200 may be performed in combination by a client device (e.g., any client device 101, 102, 103, 104, 105, and 106) and a server (e.g., server 120). Exemplarily, the specific implementation process of the video aggregation method of the embodiment of the present application is described below using a server for video aggregation as the execution subject. The specific implementation process executed by other devices is similar to the process executed by the server alone, and will not be repeated herein.

[0040] Figure 2 A flow chart of a video aggregation method according to an embodiment of the present disclosure is shown. Figure 2 As shown, method 200 includes: obtaining multiple videos to be aggregated (step 210); determining video information corresponding to each of the multiple videos, the video information including at least one of a video title, an image, and a description text, wherein the image includes text information (step 220); performing text recognition on the video information to determine one or more video tags corresponding to each of the videos (step 230); for at least one video tag among the multiple video tags corresponding to the multiple videos, determining the first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag (step 240); in response to determining that the video contents in at least two first video collections are the same, performing a deduplication operation on the at least two first video collections (step 250); and determining the collection titles of the one or more video collections remaining after the deduplication operation (step 260).

[0041] According to the embodiments of the present disclosure, a large number of video collections can be generated, and a large number of collection resources can be added to the search library, thereby increasing the amount and display of collection resources searched by users in search scenarios, and improving users' search satisfaction and consumption steps.

[0042] In step 210, a plurality of videos to be aggregated are obtained.

[0043] According to some embodiments, obtaining multiple videos to be aggregated may include: capturing multiple videos on a preset platform to form a second video collection; and classifying the videos in the second video collection by author to use multiple videos corresponding to the same author as the multiple videos to be aggregated.

[0044] According to some embodiments, before classifying the videos in the second video collection by author, the method further includes: performing a deduplication operation on the videos in the second video collection based on the link addresses of the videos.

[0045] In some cases, a search platform's video library may be primarily crawled using a crawler. During this crawling process, many sites adapt to different devices (e.g., mobile devices, desktop devices, etc.), resulting in the same content appearing on multiple sub-sites, such as v.youku and m.youku. Additionally, some authors may publish the same video twice, necessitating preprocessing of videos captured on the pre-defined platform (e.g., videos in the video library) to remove noise.

[0046] Specifically, all acquired videos can be categorized by author, and all videos from each author in the library can be placed into a collection. Deduplication can then be performed. Aggregation based on the author's granularity prevents videos of the same subject matter from being aggregated into a single collection.

[0047] For example, in the case of multi-terminal adaptation, the link address URL is split to obtain information such as the site and path, and only one of the identical paths is retained. For example, by splitting the link address URL to obtain the video ID information, the same video content on the same site has the same video ID, so deduplication can be performed based on the extracted video ID information. After the deduplication operation, the video collection corresponding to each author can be obtained, {author_id:video_set).

[0048] In step 220, video information corresponding to each of the plurality of videos is determined, wherein the video information includes at least one of a video title, an image, and a description text, wherein the image includes text information.

[0049] In some examples, the image may be the cover image of the video, or may be the corresponding picture frame obtained by framing the video; or, the image may include a combination of the cover image and the corresponding picture frame obtained by framing the video, which is not limited here.

[0050] In some examples, the description text may be a description text corresponding to the video in addition to the video title. Additionally or alternatively, text determined based on voice information in the video may also serve as the description text. For example, voice information in the video may be extracted and converted into text information using automatic speech recognition (ASR) technology.

[0051] In step 230, text recognition is performed on the video information to determine one or more video tags corresponding to each video.

[0052] According to some embodiments, Figure 3As shown, performing text recognition on the video information to determine one or more video tags corresponding to each video (step 230) includes at least one of the following steps: identifying keywords in the text information corresponding to each video to obtain the one or more video tags (step 310); identifying preset characters in the text information corresponding to each video to obtain the one or more video tags based on the preset characters, wherein the preset characters include at least one of the following items: brackets, colons, spaces, and question marks (step 320); and identifying the font size of the text in the text information corresponding to each video to obtain the one or more video tags based on the font size (step 330).

[0053] Generally speaking, when users post videos, in order to increase their appeal, there will be core keywords representing the video in the title or cover image. Therefore, keywords are extracted from the video information such as the title and cover image of the video posted by the user as tags for the video.

[0054] In some examples, images are extracted from the acquired video frame by frame, for example, one frame per second, and the text in the extracted video images is recognized using a technology such as optical character recognition (OCR). For the recognized text, a keyword extraction algorithm can be used to extract keywords. Keywords refer to words that can reflect the theme or main content of the text. Algorithms that can be used for keyword extraction include but are not limited to: TF-IDF keyword extraction method, Topic-model keyword extraction method and RAKE keyword extraction method, TextRank algorithm, LDA algorithm, TPR algorithm, Named Entity Recognition (NER) algorithm, etc.

[0055] In some examples, text information can be segmented; the segmented words are input into a trained word viscosity model to obtain the probability that each word can be connected with the next word; and words with probabilities greater than a threshold probability are screened to form key phrases as recognition results.

[0056] A key phrase refers to a typical and representative phrase in a sentence that can express the key content of the sentence. Generally, a key phrase contains multiple words. For example, "Baidu International Building" generally constitutes a key phrase, which contains the three words "Baidu", "International" and "Building". In some examples, keyword recognition and / or key phrase recognition can also be performed on the text in the pictures extracted by frame from the video, the text in the video voice, the video title, the video description, and the video comments, etc., including recognizing the text of multiple parts together, recognizing the text of each part separately, etc., without limitation here.

[0057] Through the trained word viscosity model, the probability that the two words can be connected together can be quickly determined, and then the corresponding key phrases can be quickly obtained based on the probability, with a high recognition rate.

[0058] In some examples, the core keywords of the video can be determined based on preset characters, such as within various brackets or before punctuation marks such as colons, spaces, and question marks. Furthermore, the video keywords may also be displayed in a larger font. Therefore, in some examples, the font size of the text in the text information can be sorted so that the text corresponding to the largest font is used as the video tag.

[0059] By extracting video tags from each video under the current author using the above method, we can obtain video tags of various granularities. For example, for a video titled "[XX Movie Watch] 11-minute quick look at the web drama "AA" 1-4," we can extract the video tags [XX Movie Watch] and [AA] of different granularities. Ultimately, we obtain one or more video tags corresponding to each video, namely the tag set {author_id:video_set,tag_set).

[0060] In step 240, for at least one video tag among the multiple video tags corresponding to the multiple videos, a first video collection corresponding to each video tag in the at least one video tag is determined respectively, wherein each video in the first video collection corresponds to a corresponding video tag.

[0061] Specifically, in some examples, all video tags under the current author author_id can be traversed, and for each video tag tag_set, if a video contains the video tag, it will be aggregated into the first video collection video_tag corresponding to the video tag, that is, {author_id:video_set,tag_set,video_tag).

[0062] According to some embodiments, the method according to the present disclosure may further include: for at least one video collection among the one or more video collections, fine-grained division of the videos in the video collection based on preset video tags to form a sub-video collection corresponding to the preset video tags in the video collection.

[0063] For example, for a video collection video_tag aggregated based on each tag, such as a film, television and animation collection, the trailer, highlights, theme song, ending song, interview, etc. of a certain IP will be put together, that is, the preset video tag can be the trailer, highlights, theme song, ending song, interview, etc., so that the fine-grained sub-video collection will continue to be appended to the first video collection video_tag.

[0064] In step 250 , in response to determining that video contents in at least two first video collections are identical, a deduplication operation is performed on the at least two first video collections.

[0065] Because the collections aggregated by different video tags under a certain author may have exactly the same content, it is desirable to perform a deduplication operation. Specifically, all first video collections under the current author's author_id can be traversed, and the link addresses (such as URLs) corresponding to the videos in each first video collection can be sorted (for example, alphabetically) and then spliced. If the spliced ​​link addresses are exactly the same, then the video contents in the corresponding first video collections are the same, and one of the first video collections will be retained.

[0066] In some examples, the video tags corresponding to at least two first video collections undergoing deduplication operations are merged, for example, by using a preset connecting character, such as "-", " / ", etc. For example, if the videos in the video collection determined based on the video tag [AA] and the video tag [BB] are exactly the same, only one video collection corresponding to the two video tags may be retained, and the tag corresponding to the retained video collection may be modified to [AA-BB].

[0067] In step 260, the collection titles of the one or more video collections remaining after the deduplication operation are determined.

[0068] The collection title can be used to represent the video content in the video collection. Traditionally, the video tag corresponding to the video collection can be directly used as the collection title of the video collection.

[0069] Furthermore, in order to obtain a title that is more appropriate to the video content in the video collection, according to some embodiments, determining the collection title of each of the one or more video collections remaining after the deduplication operation includes: for each of the remaining one or more video collections, determining the maximum common subsequence of the video titles corresponding to all videos in the video collection, so as to determine the collection title of the video collection based on the maximum common subsequence.

[0070] The maximum common subsequence (LCS) problem involves finding the longest subsequence among all sequences in a set of sequences (usually two sequences). A sequence is considered the longest common subsequence of two or more known sequences if it is a subsequence of each sequence and is the longest among all sequences that meet this condition.

[0071] When the number of videos and video title characters in the video collection is large, it is necessary to calculate the maximum common subsequence based on all the character strings, which requires a high computational cost. Therefore, according to some embodiments, such as Figure 4 As shown, determining the maximum common subsequence of video titles corresponding to all videos in the video collection includes: randomly selecting N video pairs in the video collection, where N is a positive integer (step 410); for each of the N video pairs, determining the maximum common subsequence of video titles corresponding to the two videos in the video pair (step 420); and determining the maximum common subsequence with the largest number of occurrences in the N video pairs as the maximum common subsequence of video titles corresponding to all videos in the video collection (step 430).

[0072] Specifically, in some examples, N (e.g., N is 5) random video pairs can be selected, and the maximum common subsequence of the video titles corresponding to the two videos in each video pair can be calculated. After determining the maximum common subsequence corresponding to the N video pairs, the maximum common subsequence with the most occurrences is voted to determine the collection title of the video collection based on the maximum common subsequence with the most occurrences. For example, the maximum common subsequence can be spliced ​​after the video collection label, such as by using a preset connection character.

[0073] According to some embodiments, determining the collection title of each of the one or more video collections remaining after the deduplication operation includes: determining the collection title based on a trained neural network model. In some embodiments, Figure 5 As shown, the neural network model can be trained based on the following method (500): obtaining the video title corresponding to the sample video (step 510); performing a masking operation on the video title corresponding to the sample video to shield the corresponding text information (step 520); inputting the masked video title into the neural network model for pre-training to obtain a pre-trained neural network model, wherein, during the pre-training process, the neural network model is used to predict the shielded text information in the input video title (step 530); obtaining the video titles corresponding to one or more videos in the sample video collection and the collection title corresponding to the sample video collection (step 540); splicing the video titles corresponding to one or more videos in the sample video collection (step 550); inputting the spliced ​​video titles into the pre-trained neural network model to obtain a predicted collection title (step 560); and adjusting the parameters of the pre-trained neural network model based on the predicted collection title and the collection title corresponding to the sample video collection to obtain the trained neural network model (step 570).

[0074] In this embodiment, a neural network model for collection title generation is trained by pre-training + downstream fine-tuning. In some examples, the classic GPT series or ERNIE series network structure can be selected as the backbone network of the neural network model.

[0075] Since the training task is a collection video title prediction task, video title data can be used for large-scale unsupervised pre-training. For example, 1 billion video title data can be mined for self-supervised training. Specifically, during the pre-training process, a mask operation is performed on the video title corresponding to the sample video so that the neural network model predicts the masked text information in the input video title. After pre-training based on domain data, the pre-trained neural network is fine-tuned downstream. During the downstream fine-tuning process, the video collection published by the existing author and the collection title of the video collection can be captured as label data, and the title of each video in the captured video collection is used as input. Specifically, the video titles corresponding to one or more videos in the captured video collection are spliced, such as splicing the video titles by specifying special characters as separators through the sep parameter. For each video collection, the spliced ​​video titles are input into the pre-trained neural network model to obtain the predicted collection title.

[0076] According to the embodiments of the present disclosure, Figure 6 As shown, a video aggregation device 600 is also provided, including: an acquisition unit 610, configured to acquire multiple videos to be aggregated; a first determination unit 620, configured to determine the video information corresponding to each of the multiple videos, wherein the video information includes at least one of a video title, an image, and a description text, wherein the image includes text information; a second determination unit 630, configured to perform text recognition on the video information to determine one or more video tags corresponding to each of the videos; a third determination unit 640, configured to determine, for at least one video tag among the multiple video tags corresponding to the multiple videos, a first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag; a first deduplication unit 650, configured to perform a deduplication operation on the at least two first video collections in response to determining that the video contents in at least two first video collections are the same; and a fourth determination unit 660, configured to determine the collection titles of the one or more video collections remaining after the deduplication operation.

[0077] Here, the operations of the above-mentioned units 610-660 of the video aggregation device 600 are similar to the operations of steps 210-260 described above, and are not repeated here.

[0078] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0079] According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0080] refer to Figure 7 , a block diagram of an electronic device 700 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0081] like Figure 7 As shown, electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of electronic device 700 can also be stored in RAM 703. Computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0082] Multiple components within electronic device 700 are connected to I / O interface 705, including an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. Input unit 706 can be any type of device capable of inputting information into electronic device 700. Input unit 706 can receive input numeric or character information and generate key signal input related to user settings and / or function control of the electronic device. It can include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 707 can be any type of device capable of presenting information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 708 can include, but is not limited to, a magnetic disk or an optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks. It can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0083] The computing unit 701 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method 200 described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0084] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0088] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0089] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0090] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0091] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A video aggregation method, comprising: Get multiple videos to be aggregated; Determining video information corresponding to each of the plurality of videos, wherein the video information includes at least one of a video title, an image, and a description text, wherein the image includes text information; Performing text recognition on the video information to determine one or more video tags corresponding to each video; For at least one video tag among the multiple video tags corresponding to the multiple videos, respectively determine a first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag; In response to determining that the video contents in the at least two first video collections are identical, performing a deduplication operation on the at least two first video collections; and Determine the collection titles of the one or more video collections remaining after deduplication, including: For each of the remaining one or more video collections, determining the longest common subsequence of video titles corresponding to all videos in the video collection, and determining the collection title of the video collection based on the longest common subsequence; or, The collection title is determined based on a trained neural network model, wherein the neural network model is trained based on the following operations: Get the video title corresponding to the sample video; Performing a masking operation on the video title corresponding to the sample video to shield corresponding text information; Inputting the masked video title into a neural network model for pre-training to obtain a pre-trained neural network model, wherein during the pre-training process, the neural network model is used to predict the masked text information in the input video title; Obtaining video titles corresponding to one or more videos in a sample video collection, and a collection title corresponding to the sample video collection; splicing video titles corresponding to one or more videos in the sample video collection; Inputting the spliced ​​video titles into the pre-trained neural network model to obtain a predicted collection title; and The parameters of the pre-trained neural network model are adjusted based on the predicted collection title and the collection title corresponding to the sample video collection to obtain the trained neural network model.

2. The method of claim 1, wherein Obtaining multiple videos to be aggregated includes: Capturing multiple videos on a preset platform to form a second video collection; and The videos in the second video collection are classified according to the authors, so that multiple videos corresponding to the same author are used as the multiple videos to be aggregated.

3. The method according to claim 2, wherein: Before classifying the videos in the second video collection by author, the method further includes: performing a deduplication operation on the videos in the second video collection based on the link addresses of the videos.

4. The method according to claim 1, wherein Performing text recognition on the video information to determine that one or more video tags corresponding to each video include at least one of the following: Identifying keywords in the text information corresponding to each video to obtain the one or more video tags; Identifying preset characters in the text information corresponding to each video to obtain the one or more video tags based on the preset characters, wherein the preset characters include at least one of the following: brackets, colons, spaces, and question marks; as well as The font size of the text in the text information corresponding to each video is identified to obtain the one or more video tags based on the font size.

5. The method of claim 1 , further comprising: For at least one video collection among the one or more video collections, fine-grained division is performed on the videos in the video collection based on preset video tags to form a sub-video collection corresponding to the preset video tag in the video collection.

6. The method of claim 1, wherein: Determine the longest common subsequence of video titles corresponding to all videos in the video collection, including: Randomly select N video pairs from the video collection, where N is a positive integer; For each of the N video pairs, determining the longest common subsequence of video titles corresponding to the two videos in the video pair; and The longest common subsequence that appears the most times in the N video pairs is determined as the longest common subsequence of the video titles corresponding to all videos in the video collection.

7. A video aggregation device, comprising: an acquisition unit, configured to acquire a plurality of videos to be aggregated; a first determining unit configured to determine video information corresponding to each of the plurality of videos, wherein the video information includes at least one of a video title, an image, and a description text, wherein the image includes text information; a second determining unit configured to perform text recognition on the video information to determine one or more video tags corresponding to each video; a third determining unit configured to determine, for at least one video tag among the multiple video tags corresponding to the multiple videos, a first video collection corresponding to each video tag in the at least one video tag, wherein each video in the first video collection corresponds to a corresponding video tag; a first deduplication unit configured to, in response to determining that video contents in at least two first video collections are identical, perform a deduplication operation on the at least two first video collections; and A fourth determining unit is configured to determine the collection title of each of the one or more video collections remaining after the deduplication operation, wherein the fourth determining unit includes: a determining subunit configured to determine, for each of the remaining one or more video collections, a longest common subsequence of video titles corresponding to all videos in the video collection, so as to determine a collection title of the video collection based on the longest common subsequence; or and means for determining the collection title based on a trained neural network model, wherein the neural network model is trained based on the following operations: Get the video title corresponding to the sample video; Performing a masking operation on the video title corresponding to the sample video to shield corresponding text information; Inputting the masked video title into a neural network model for pre-training to obtain a pre-trained neural network model, wherein during the pre-training process, the neural network model is used to predict the masked text information in the input video title; Obtaining video titles corresponding to one or more videos in a sample video collection, and a collection title corresponding to the sample video collection; splicing video titles corresponding to one or more videos in the sample video collection; Inputting the spliced ​​video titles into the pre-trained neural network model to obtain a predicted collection title; and The parameters of the pre-trained neural network model are adjusted based on the predicted collection title and the collection title corresponding to the sample video collection to obtain the trained neural network model.

8. The device according to claim 7, wherein The acquisition unit includes: a unit for capturing a plurality of videos on a preset platform to form a second video collection; and Used to classify the videos in the second video collection according to the author, so as to use multiple videos corresponding to the same author as the units of the multiple videos to be aggregated.

9. The device according to claim 8, wherein Before the videos in the second video collection are classified by author, the method further includes: a second deduplication unit configured to perform a deduplication operation on the videos in the second video collection based on the link addresses of the videos.

10. The device according to claim 7, wherein The second determining unit includes at least one of the following items: a unit for identifying keywords in the text information corresponding to each video to obtain the one or more video tags; a unit for identifying preset characters in the text information corresponding to each video to obtain the one or more video tags based on the preset characters, wherein the preset characters include at least one of the following: a bracket, a colon, a space, and a question mark; as well as A unit configured to identify a font size of text in the text information corresponding to each video, so as to obtain the one or more video tags based on the font size.

11. The apparatus of claim 7, further comprising: The classification unit is configured to perform fine-grained division of videos in at least one video collection among the one or more video collections based on preset video tags, so as to form a sub-video collection corresponding to the preset video tag in the video collection.

12. The device according to claim 7, wherein The determining subunit includes: A unit for randomly selecting N video pairs from the video collection, where N is a positive integer; a unit for determining, for each of the N video pairs, a longest common subsequence of video titles corresponding to two videos in the video pair; and A unit for determining the longest common subsequence that appears the most times in the N video pairs as the longest common subsequence of video titles corresponding to all videos in the video collection.

13. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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