Patent Publication Number: US-8990065-B2

Title: Automatic story summarization from clustered messages

Description:
BACKGROUND 
     Within the field of computing, many scenarios involve a set of stories related in one or more messages. As a first example, breaking news may be covered by several news outlets, each of which may generate a stream of articles summarizing the news stories. As a second example, a set of events occurring within a community may be described in blog posts among the members of the community. As a third example, a group of friends may post messages within a social network describing various stories arising within the group. In these and other scenarios, the stories may be automatically aggregated into a story feed; e.g., a news server may be configured to retrieve the messages generated by a set of message sources (e.g., one or more news outlets) relating a set of stories, and to present the retrieved messages as an aggregated news feed. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     In scenarios involving an automated retrieval of messages from various message sources that present one or more stories, different message sources (such as authors or news outlets) may report the facts of a particular story in different ways. For example, the facts of the story may be related haphazardly within messages describing the story; e.g., a commentary on a particular story may allude to some facts throughout the narrative, but may not relate the entire story in a cohesive manner. An update or commentary on a story may also link or refer to another message that describes a story, and may add facts to the story, but may not recapitulate the original facts of the story. Additionally, the facts may be related in different ways based on the perspective of the author in reporting the story; e.g., authors writing from different political backgrounds may use very different terminology and phrasing to cover the same news story. As yet another example, different message sources may cover a news story in different languages, such as different cultural languages (e.g., English and Spanish), different literacy levels, different tone (e.g., a formal report of a story and a colloquial eyewitness report), or different target audiences (e.g., a medical study reported in a scientific journal and a popular description of the same study). 
     In order to provide readers with a summary of a story, it may be desirable to develop an automated technique for summarizing a particular story. An automated technique may be able to extract cohesive summaries of stories without bias, and may scale to handle a large volume and rate of messages. However, it may be difficult for an automated technique to extract all of the relevant facts of a story from a particular message, which may omit some facts, or may describe some facts in an ambiguous or unusual manner that is difficult to recognize in an automated manner. This difficulty may be overcome by summarizing each stories based on all of the messages that relate the story. For example, while different authors may use different terminology or language to describe a particular story, an automated evaluation may identify a set of terms or phrases that often arise within messages about the story, and may generate a summary of the story using such frequently arising terms and phrases. It may therefore be desirable to devise an automated summarizing technique that includes a clustering of messages about a particular story, and that evaluates all such messages together to identify the basic facts of the story and to generate a summary from such basic facts. Moreover, it may be desirable to design such automated techniques in an extensible manner, e.g., in a manner that allows an adjustment of the summarizing process for a particular set of messages, and the introduction of new processing techniques. 
     Presented herein are techniques for automatically extracting the facts of a story in order to provide readers with a cohesive, unbiased summary of the story. In accordance with these techniques, an automated message processing technique may evaluate a set of messages, and may cluster the messages based on the stories that are related in such messages. For a particular story, all of the messages relating to the story may be evaluated together, the entities, facts, and phrases that appear frequently in such messages may be extracted as a summary of the story. The summary may be utilized, e.g., as a headline or title of the story; may be presented with messages associated with the story to provide additional context; and/or may facilitate searches for messages associated with a particular story, topic, or political, sociological, religious, and/or philosophical perspective. Additionally, the summaries generated from the facts of the stories may be language- and perspective-independent, and the automated generation may scale well to summarize a large number of stories related in a large volume of messages (e.g., millions of messages presented in a social network on a particular day). 
     Additionally, the processing techniques presented herein may be devised as an architecture comprising message processing pipeline, e.g., wherein each message is subjected to a series of processing components, each configured to transform the message, identify concepts within the message, and/or extract particular facts from the message that may be included in the summary of the story. Implementing the message processing techniques presented herein as a message processing pipeline may promote the adjustment of the evaluation in view of different message sources (e.g., choosing a first message processing pipeline using a first series of components for a first message source, such as a formal news source, and a second message processing pipeline using a different series of components for a second message source, such as a colloquial personal weblog), and may facilitate the customization of message processing with custom processing components (e.g., a particular language component to translate slang phrases that frequently arise within a particular community). 
     To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  presents an illustration of an exemplary scenario featuring a keyword-based evaluation and filtering of messages associated with various stories. 
         FIG. 2  presents an illustration of an exemplary scenario featuring an automated generation of summaries of stories using facts extracted from messages associated with respective stories in accordance with the techniques presented herein. 
         FIG. 3  presents another illustration of an exemplary scenario featuring an automated generation of summaries of stories using facts extracted from messages associated with respective stories in accordance with the techniques presented herein. 
         FIG. 4  is a flow chart illustrating an exemplary method of generating summaries of stories respectively associated with at least one message. 
         FIG. 5  is a component block diagram illustrating an exemplary message processing pipeline for generating summaries of stories respectively associated with at least one message. 
         FIG. 6  is an illustration of an exemplary computer-readable medium comprising processor-executable instructions configured to embody one or more of the provisions set forth herein. 
         FIG. 7  is an illustration of an exemplary scenario featuring a message processing pipeline configured to process messages of different languages. 
         FIG. 8  is an illustration of an exemplary scenario featuring an identification of a story perspective of respective stories relating a particular story and the message source perspectives of the message sources of the messages. 
         FIG. 9  illustrates an exemplary computing environment wherein one or more of the provisions set forth herein may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter. 
     Within the field of computing, many scenarios involve a set of messages that may relate one or more stories. As a first example, a set of news articles may relate information about various breaking news stories, or may update previously reported stories with additional information. As a second example, a weblog or set of weblogs may present weblog posts generated by authors about a wide variety of stories that relate to one or more topics comprising a theme of the weblog and/or that relate personal stories of the author. As a third example, within a social network, a group of contacts may exchange public, private, and semi-private messages about various topics, such as stories occurring within a group of friends. 
     In these and other examples, the messages may be aggregated into a message set. However, a user viewing the message set may not wish to see all of the messages, but may have difficulty identifying the stories or topics related to respective messages in an efficient manner (e.g., without having to read a substantial portion of the message). The user may also wish to filter the message set to a subset of stories or topics of interest, or to view stories having a particular perspective (e.g., written from the viewpoint of a particular political, sociological, religious, and/or philosophical position, or from an author whose messages often espouse such a position). In order to satisfy the viewing interests of the user, various techniques may be applied to categorize the messages, to relate messages to particular stories or topics, and/or to identify various metadata for each message that may be utilized to filter the message set in a desirable manner. 
       FIG. 1  presents an exemplary scenario  10  featuring one such technique for filtering a set of messages  16  relating to a set of stories, such as a first story  12  involving a newly developed disease therapy; a second story  12  involving a new movie to be released in theaters; and a third story  12  involving a tax cut enacted by a government. In this exemplary scenario  10 , each message  16  may have been generated by a message source  14  (e.g., a journalist, an eyewitness of an event, or a commentator), who may generate the message  16  in relation to one or more stories  12 . Moreover, each message  16  may have been authored with a particular story perspective on the story  12 . As a first example, different story perspectives may relate to different types of target audiences; e.g., with respect to the first story  12  regarding a newly developed therapy for a particular disease, a first message source  14  may generate a first message  16  targeted at the general community with simple language and a practical perspective, while a second message source  14  may generate a second message  16  targeted to the scientific community with complex facts and a technical perspective. As a second example, different story perspectives may relate to different personal opinions; e.g., with respect to the second story  12  regarding a new movie, a first message source  14  may generate a third message  16  featuring a positive review of the movie, while a second message source  14  may generate a fourth message  16  featuring a negative review of the movie. As a third example, different story perspectives may relate to different political philosophies; e.g., with respect to the third story  12  regarding a recently enacted tax cut, a first message source  14  may generate a fifth message  16  written from the perspective of a fiscal progressive and promoting the economy stimulating effects of the tax cut, while a second message source  14  may generate a sixth message  16  written from the perspective of a fiscal conservative and noting the increased government deficit ensuing from the tax cut. 
     The different content and story perspectives of different messages  16  may add color and variety to the message set in reporting the stories  12 , and a user  22  may find some messages  16  more satisfying than others. In particular, the user  22  may wish to view messages  16  having a particular characteristic, such as relating to a particular story  12  or to particular topics, or written with a particular story perspective (e.g., political news stories written from a particular political philosophy). In order to fulfill a request of a user  22  to view particular messages  16 , it may be desirable to utilize an automated technique to evaluate and categorize stories, which may efficiently and cost-effectively scale to handle the evaluation of a large volume of messages  16  (e.g., millions of messages  16  exchanged daily within a social network). In the exemplary scenario  10  of  FIG. 1 , an automated classifier  18  is configured to identify keyword keywords  20  within respective messages  16  that may indicate the topics, stories  12 , and/or story perspectives related to each message  16 . For example, within the first message  16 , keywords may be identified relating to the name of the disease treated, the name of the newly developed therapeutic technique (e.g., a chemical or commercial name of a new drug), and/or the names of the researchers involved. The automated classifier  18  may also classify the first message  16 , e.g., as belonging within the field of science. When a user  22  submits a request  24  to view messages  16  involving a particular keyword  20 , the automated classifier  18  may retrieve the messages  16  identified as including the keyword  20  and may provide such messages to the user  22 . 
     However, the use of keywords  20  to identify messages  16  relating to particular stories  12 , topics (such as individuals), or story perspectives may present some limitations. As a first example, variations in the content of the messages  16  (e.g., differences in cultural languages, target audiences, personal opinions, and formal or colloquial tone of the message  16 ) may result in variations in the keywords  20  included in different messages  16  relating to the same story  12  and/or topic. As a second example, a first message  16  may refer to a second message  16  (e.g., a news update of a previously reported story, or a weblog post or social network message authored as a response to an earlier weblog post or social network message), and may therefore relate to the same stories  12 , topics, and/or story perspectives related in the second message  16 , but the first message  16  may not explicitly reiterate the details of the second message  16  and therefore may not include the same keywords  20 . As a third example, even keywords  20  for topics identified by proper nouns (e.g., distinctive names of individuals or locations) may vary due to the use of slang or nicknames, or the presence of typographical errors. For at least these reasons, fulfilling the request of the user  22  to view messages  20  associated with a particular story  12  based on a comparison of keywords  20  may be inadequate in identifying the content of the respective messages  16 , and in providing requested messages  16  to the user  22 . As a second exemplary limitation, the user  22  may simply wish to browse the messages  16 , but may have difficulty identifying the content of any particular message  16  without having to read a substantial portion thereof. For example, more formal messages  16  (e.g., news articles posted by a news source) may include a human-generated headline, but the headline may be inadequate (e.g., a headline reading “New Tax Cuts Enacted” may fail to indicate the locality, extent, and date of the tax cuts), and/or may have been authored from a story perspective that obstructs inferences as to the contents of the message  16 . Additionally, presenting the keywords  20  with the message  16  may be inadequate to convey to the user  22  the contents of the message  16  (e.g., presenting the detected keywords “tax cut, economy” with a message  16  may not adequately convey to the user  22  the particular tax cuts discussed, or the relationship between the tax cut and the economy that is suggested by the message  16 ), and may therefore have to read a substantial portion of the message  16  to identify the content presented therein. 
     In view of these disadvantages, it may be desirable to configure an automated classifier to evaluate the semantics of respective messages  16  in order to identify the stories  12 , topics, and story perspective related by each message  16 . In particular, it may be desirable to generate an automated classifier  32  that is capable of identifying the facts of respective messages  16  and automatically generating a summary of a story  12  referenced thereby. Moreover, it may be advantageous to generate a summary of a story  12  based on several messages  16  relating the story  12 , where such messages  16  are generated by a set of message sources  14  (e.g., different authors or news organizations). By using several messages  16 , an automated technique may identify facts that are frequently related in many messages  16  generated by authors  14  having a wide range of perspectives, writing in a wide variety of languages, and writing for a wide range of target audiences, and may therefore use the frequently reported facts to generate the summary of the story  12  in a perspective-, language-, and target-audience-independent manner. 
       FIG. 2  presents an exemplary scenario  30  wherein a set of messages  16  of a message set (e.g., a news source or a social network) are generated by a set of message authors  14  relating to various stories  12 . In this exemplary scenario  30 , an automated classifier  32  configured according to the techniques presented herein may endeavor to generate a summary  36  of each story  12  by evaluating the messages  16  of the message set in the following manner. The automated classifier  32  first identifies the stories  12  associated with respective messages  16 . For each story  12 , the messages  16  relating to the story  12  are evaluated together, and facts  34  that are frequently reported in such messages  16  are identified. From these facts  34 , a summary  36  may be generated, and may be utilized, e.g., as a title or headline of the story  12 . The summaries  36  may then be presented to a user  22  in order to facilitate the rapid evaluation of the messages  16 , and/or may be used to satisfy a request  24  from the user  22  to present messages  16  associated with a particular story  12 , topic, or story perspective. 
       FIG. 3  presents an exemplary scenario  40  featuring some additional detail as to the generation of a summary  36  of a story  12 . In this exemplary scenario  40 , two stories  12  are related in three messages  16 , each generated by a particular message author  14 . An automated classifier, configured according to the techniques presented herein, may generate a summary  36  of each story  12  in the following manner. First, from each message  16 , a set of entities  42  are identified, such as topics, concepts, objects, individuals, events, and locations referenced by the message  16 . For example, from a message  16  featuring a positive review of a movie, the entities  42  extracted may include the movie, an actor included in the cast of the movie, and a novel upon which the movie is based. From a second message involving a negative review of the same movie, a similar set of entities  42  may be extracted (possibly including the author of the novel upon which the movie was based, even if the novel is not explicitly referenced); but from a third message relating to the effects of a recently enacted tax cut on the economy, the extracted entities  42  may include the tax cut, the economy, and the political body that enacted the tax cut (e.g., the United States Senate). The entities  42  extracted from each message  16  are compared, and from this comparison, one or more message clusters  44  are identified, comprising messages  16  involving a similar set of entities  42  and likely relating to the same story  12 . For example, a first message cluster  44  may be identified comprising the messages  16  associated with the story  12  about the release of the movie, and a second message cluster  44  may be identified comprising the message  16  associated with the story  12  of the tax cut. The messages  16  within each message cluster  44  may be evaluated together to identify a set of facts  34  that appear frequently in the messages  16 . For example, this evaluation may be reveal that messages  16  relating to the story  12  of the release of the new movie often mention that the movie is to be released in theaters on an upcoming Friday; that the movie features a particular actor named Joe Smith  34 ; and that the movie is based on a popular novel by the author Sam Beckett. Based on these frequently reported facts  34 , the automated classifier may automatically generate a summary  36 . 
     These summaries  36  generated in the exemplary scenario  40  of  FIG. 3  (and in accordance with the techniques presented herein) may be used in various ways. For example, the summaries  36  presented to users  22  as a summary, title, or headline of the respective stories  12 , and may be displayed with various messages  16  to present additional context (e.g., to relate the facts of a story  12  that are referenced or implied by the message  16 ). Moreover, the summary  36  generated from the facts  34  extracted from many messages  16  may be independent of the story perspectives of the messages  16 , including the political, sociological, religious, and/or philosophical perspectives of the message sources  14  of the messages  16 . These summaries  36  may also be less dependent upon the keywords  20  that may or may not be included in each message  16 ; e.g., the first message  16  may omit the name of the author of the novel, and the second message  16  may omit the name of the novel, but both messages  16  may be associated with the same story  12  that involves all of these facts  34 , and may therefore be included in searches submitted by users  22  involving such facts  34  (e.g., the first message  16  may be presented in response to a search for messages  16  and stories  12  about the novel, even though the name of the novel is not mentioned in the first message  16 , because the first message  16  is identified as associated with the story  12  including a fact  34  referencing the novel). Additionally, the automated generation of summaries  36  may efficiently and cost-effectively scale to handle the categorization of a large volume of messages  16 , such as millions of messages  16  posted within a social network on a particular day. These and other advantages may be achieved through the automated generation of summaries  36  of stories  12  according to the techniques presented herein. 
       FIG. 4  presents a first embodiment of these techniques, illustrated as an exemplary method  50  of generating summaries  36  of stories  12  respectively associated with at least one message  16 . The exemplary method  50  may be implemented, e.g., as a set of software instructions stored in a memory component (e.g., a system memory circuit, a platter of a hard disk drive, a solid state storage device, or a magnetic or optical disc) of the computer  12 , that, when executed by the processor of the computer  12 , cause the processor to perform the techniques presented herein. The exemplary method  50  begins at  52  and involves executing  54  the instructions on the processor. More specifically, the instructions are configured to, for respective messages  16 , identify  56  at least one entity  42  involved in the message  16 . The instructions are also configured to compare  58  the entities  42  involved in respective messages  16  to identify a message cluster  44 , comprising the messages  16  associated with a particular story  12 . The instructions are also configured to, using the messages  16  of the message cluster  44 , identify  60  at least one fact  34  of the story  12 . The instructions are also configured to, using the at least one fact  34  of the story  12 , generate  62  a summary  36  of the story  12 . In this manner, the exemplary method  50  achieves an automated generation of a summary  36  of the story  12 , and so ends at  64 . 
       FIG. 5  presents a second embodiment of the techniques presented herein, illustrated as a message processing pipeline  76  configured to generate summaries  36  of stories  12  respectively associated with at least one message  16 . The message processing pipeline  76  may be implemented, e.g., as a software architecture, comprising a series of interoperating components that in turn evaluate a message  16  to perform one or more tasks involved in automatically generating the summary  36  of the story  12 . Each component of the message processing pipeline  76 , in turn, may be implemented as a set of software instructions stored in a memory component (e.g., a system memory circuit, a platter of a hard disk drive, a solid state storage device, or a magnetic or optical disc) of a device  72 , where the instructions, when executed (concurrently or consecutively) by the processor of the device  72 , cause the processor  74  to perform one or more tasks of the techniques presented herein. The message processing pipeline  76  includes an entity extraction component  78 , which is configured to, for respective messages  16 , identify at least one entity  42  involved in the message  16 . The message processing pipeline  76  also includes a message clustering component  80 , which is configured to compare the entities  42  involved in respective messages  16  to identify a message cluster  44  comprising the messages  16  associated with a particular story  12 . The message processing pipeline  76  also includes a summary generating component  82 , which is configured to, using the messages  16  of the message cluster  44 , identify at least one fact  34  of the story  12 ; and, using the facts  34  of the story  12 , generate a summary  36  of the story  12 . In this manner, the message processing pipeline  76  automatically generates summaries of the stories  36  from the facts  34  frequently reported in the set of messages  16  associated with the story  12 . 
     Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to apply the techniques presented herein. Such computer-readable media may include, e.g., computer-readable storage media involving a tangible device, such as a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. Such computer-readable media may also include (as a class of technologies that are distinct from computer-readable storage media) various types of communications media, such as a signal that may be propagated through various physical phenomena (e.g., an electromagnetic signal, a sound wave signal, or an optical signal) and in various wired scenarios (e.g., via an Ethernet or fiber optic cable) and/or wireless scenarios (e.g., a wireless local area network (WLAN) such as WiFi, a personal area network (PAN) such as Bluetooth, or a cellular or radio network), and which encodes a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. 
     An exemplary computer-readable medium that may be devised in these ways is illustrated in  FIG. 6 , wherein the implementation  900  comprises a computer-readable medium  902  (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data  904 . This computer-readable data  904  in turn comprises a set of computer instructions  906  configured to operate according to the principles set forth herein. In one such embodiment, the processor-executable instructions  906  may be configured to perform a method of generating summaries of stories respectively associated with at least one message, such as the exemplary method  50  of  FIG. 4 . In another such embodiment, the processor-executable instructions  906  may be configured to implement a message processing pipeline for generating summaries of stories respectively associated with at least one message, such as the exemplary message processing pipeline  76  of  FIG. 5 . Some embodiments of this computer-readable medium may comprise a nontransitory computer-readable storage medium (e.g., a hard disk drive, an optical disc, or a flash memory device) that is configured to store processor-executable instructions configured in this manner. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein. 
     The techniques discussed herein may be devised with variations in many aspects, and some variations may present additional advantages and/or reduce disadvantages with respect to other variations of these and other techniques. Moreover, some variations may be implemented in combination, and some combinations may feature additional advantages and/or reduced disadvantages through synergistic cooperation. The variations may be incorporated in various embodiments (e.g., the exemplary method  50  of  FIG. 4  and the exemplary message processing pipeline  76  of  FIG. 5 ) to confer individual and/or synergistic advantages upon such embodiments. 
     A first aspect that may vary among embodiments of these techniques relates to the scenarios wherein these techniques may be utilized. As a first example, these techniques may be utilized to evaluate many types of messages  16 , including news articles provided by various news sources; weblog posts generated by various weblog authors; web forum messages posted in various web forums; public, semi-private, and/or private messages posted in various social networks; and email messages sent and/or received by one or more users  22 . As a second example, these techniques may identify many types of stories  12  to which such messages  16  relate, including breaking news stories; descriptions of past, present, and/or future events; fictional stories; personal anecdotes; commentaries on various political, sociological, religious, and/or philosophical topics; and reviews of various products, services, and media. As a third example, these techniques may, while evaluating the messages  16 , extract many types of entities  34  therefrom, including individuals, locations, objects, devices, machines, events, bodies of knowledge, concepts, and collections of data. Those of ordinary skill in the art may devise many scenarios wherein the techniques presented herein may be advantageously utilized. 
     A second aspect that may vary among embodiments of these techniques relates to the architecture of the embodiments. Some embodiments may be configured simply as a process (e.g., the exemplary method  50  of  FIG. 4 ), wherein a fixed set of tasks may be applied to the messages  16  to generate summaries  36  of the stories  12  related therein. However, it may be appreciated that the wide variety of scenarios wherein the techniques may be utilized may encourage a more robust architecture of an embodiment of these techniques that may be adjusted to account for the wide variety of messages  16 . Therefore, in some scenarios, it may be advantageous to devise the architecture of an embodiment as a message processing pipeline, such as the message processing pipeline  76  in the exemplary scenario  70  of  FIG. 5 , comprising a modular set of interoperating components, each of which performs one or more tasks upon respective messages  16  being processed by the embodiment. For example, in order to apply the message processing pipeline  76  to the messages  16  of a particular message source  14 , the message processing pipeline  76  may be adjusted, e.g., by swapping out some components with other components that are specially customized to evaluate messages  16  from this message source  14 , or from the messages sources  14  of a particular type (e.g., a first component or set of components configured to evaluate messages  16  of a technical nature published in an academic journal, and a second component or set of components configured to evaluate messages  16  of a casual nature published in a social network). Additionally, the architecture of a message processing pipeline  76  may more readily permit the insertion of new modules to provide additional features, such as an evaluation of new types of metadata about the messages  16 . For example, the message processing pipeline  76  may include a message processing pipeline customizing component, which may be configured to, upon receiving a custom component to be inserted into the message processing pipeline  76 , insert the custom component into the message processing pipeline  76 . The architectural flexibility provided by a message processing pipeline  76  may advantageously promote the application of such an embodiment to a wider range of message sources  14 , and/or the customization of the evaluation to perform additional types of processing. Those of ordinary skill in the art may devise many types of architectures for embodiments of the techniques presented herein. 
     A third aspect that may vary among embodiments of these techniques relates to the manner of extracting entities  42  from various messages  16  (e.g., the configuration of an entity extraction component  78 ). Various text processing, lexical, linguistic, and/or statistical techniques may be utilized to achieve this extraction of entities  42 . For example, keyword analysis may be utilized to identify keywords  20  appearing in the message  16 . Such keywords  20  may be identified using linguistic techniques; e.g., the capitalization of words may indicate a proper noun naming an entity  42 . Additionally, for respective messages  16  of the message cluster  42 , an entity extraction component  78  may identify respective sentences, and may identify a set of tokens within each message  16  (e.g., from the phrase “Joe Smith rode a recumbent bicycle across the Golden Gate Bridge,” the tokens “Joe Smith,” “recumbent bicycle,” and “Golden Gate Bridge” may be identified, optionally including the interstitial tokens “rode,” “a,” “across,” and “the”). For respective tokens a speech part may be identified (e.g., a noun, verb, adjective, adverb, article, preposition, conjunction, or interjection), and various entities  42  may be extracted from the identified tokens. Alternatively or additionally, facts  34  may be extracted from the tokens, optionally including tokens that are not necessarily associated with entities  42  (e.g., the token “jogging” may not be associated with a particular entity, but if many messages  16  about a story  12  mention that a particular individual, such as a celebrity, was spotted jogging in a particular location, the token “jogging” may be included in a fact  34  about the story  12 ). Alternatively or additionally, metadata may be utilized to extract entities  42  involved in a message  16 , such as one or more hashtags associated with a message  16  by a message source  14 , or various metadata items about the message  16  (e.g., for a recurrent event, a date on which the message  16  was authored or published may indicate a particular instance of the event to which a story  12  relates). Those of ordinary skill in the art may identify many ways of extracting entities  42  and facts  34  from messages  16  in accordance with the techniques presented herein. 
     A fourth aspect that may vary among embodiments of these techniques relates to the evaluation of messages  16  authored in different languages. Such languages may be cultural (e.g., English, French, and Spanish), tonal (e.g., a formal language of a news organization and a colloquial language used within a social network), or based on different target audiences and/or literacy levels (e.g., a report of a scientific story for the general population, as compared with a report of the same scientific story in an academic journal). An embodiment of these techniques may encounter messages  16  authored in many such languages, and may be configured to handle the evaluation of such messages  16  in various ways. For example, a message processing pipeline  76  may include a language identifying component, which may be configured to, for respective messages  16 , identify a language of the message  16  (e.g., based on linguistic analysis or a comparison of the vocabulary of the message  16  with frequently used words in respective languages). The message processing pipeline  76  may then utilize the identified language of the message  16  in various ways. As a first example, the message processing pipeline  76  may define a target language (e.g., a particular language supported by the message processing pipeline  76 ), and may simply discard all messages  16  authored in languages other than the target language. As a second example, the language identifying component may, upon identifying a source language of a message that is different from a target language, automatically translate the message  16  from the source language to the target language before further processing the translated message  16 . As a third example, the message processing pipeline  76  may include one or more multilingual components, featuring two or more language subcomponents that are configured to apply a particular task to messages  16  respectively authored in each of two or more languages (e.g., a first language subcomponent configured to process messages  16  in a first language, and a second language subcomponent configured to process messages  16  in a second language). The message processing pipeline  76  may therefore process a message  76  authored in a particular language by invoking a corresponding a language subcomponent for the language. Using any of these techniques or a combination thereof, the message processing pipeline  76  may therefore evaluate a set of messages  16  authored in a wide range of languages, and may associate messages  16  written in different languages with the same story  12 . 
       FIG. 7  presents an illustration of an exemplary scenario  100  featuring a message processing pipeline  76  configured to evaluate messages  16  in various languages. In this exemplary scenario  100 , the message processing pipeline  76  may identify a target language  102  in which the message processing pipeline  76  is natively configured to process messages  16 . Upon receiving a message  16 , the message processing pipeline  76  may first apply a language identifying component  104 , which may examine the content of the message  16  to identify a language of the content of the message  16 . For example, the message processing pipeline  76  may identify a target language  102  of English, but may receive a first message  16  written in French and a second message  16  written in Spanish. However, instead of discarding the messages  16  not written in the target language  102 , the message processing pipeline  76  of this exemplary scenario  100  endeavors to include the messages  16  in the evaluation of stories  12  related thereto. For example, the language identifying component  104  may also perform an automated translation  106  of the second message  16  in order to generate a translated message  108  in the target language  102 , which may be natively evaluated by the remaining components of the message processing pipeline  76 . Additionally, one or more components of the message processing pipeline  76  may comprise a multilingual component, featuring two or more language subcomponents that are configured to apply a particular task to messages  16  respectively written in two or more languages. For example, the entity extraction component  78  of the message processing pipeline  76  may include a first language subcomponent  108  configured to extract entities  42  from messages  16  written in French and a second language subcomponent  108  configured to extract entities  42  from messages  16  written in English. To the first message  16  (in French) and the translated message  108  (in English), the entity extraction component  78  may respectively apply the first language subcomponent  108  and the second language subcomponent  108 , and may thereby extract entities from the messages  108  in a similar manner irrespective of the languages presented by each message  16 . Those of ordinary skill in the art may devise many ways of evaluating messages  16  written in different languages in accordance with the techniques presented herein. 
     A fifth aspect that may vary among embodiments of these techniques relates to additional features that may be included in an evaluation of messages  16 , e.g., additional processing and/or transformation of a message  16  that may promote the extraction of entities  42  and facts  34 , and/or the identification of additional metadata about respective messages  16  that may be utilized while fulfilling a request  24  of a user  22  to view particular types of messages  16 . As previously discussed, the modular nature of a message processing pipeline architecture may promote the flexibility of an embodiment to incorporate such additional features, e.g., by permitting a new component to be inserted into the evaluation process to implement the new feature. 
     As a first variation of this fifth aspect, an embodiment of these techniques may be applied to evaluate messages  16  of a message source  14  that often includes various forms of slang (e.g., unusual words, nicknames, or acronyms that are familiar to a particular community, but that may be unusual or difficult to understand outside of the community). Accordingly, an embodiment of these techniques may include a slang translating component, which may be configured to identify at least one slang phrase of respective messages  16  according to a language of the message  16  (e.g., the particular language of a community), and to translate the slang phrase into at least one token of the message  16  (e.g., expanding a nickname for a particular individual into a proper name that may be associated with an entity  42 ). 
     As a second variation of this fifth aspect, various messages  16  may include references to various entities  42 , but some such references may be ambiguous. For example, the token “Bill” included in a first message  16  may refer to a particular friend within a social group, but in a second message  16  comprising a movie review may refer to an actor starring in the movie, and in a third message  16  involving a political story may relate to a particularly significant piece of legislation. Therefore, an embodiment of these techniques may include an entity resolution component, which may be configured to, for respective entities  42  identified by the entity extraction component  78 , identify at least one ambiguous reference of a message  16  identifying one of at least two possible entities  42 , and among the possible entities  42 , identify the entity  42  identified by the ambiguous reference of the message  16 . For example, when a particularly ambiguous token is detected in a message  16 , the entity resolution component may be invoked, which may evaluate the context of the message  16  and identify the entity  42  referenced by the ambiguous token. 
     As a third variation of this fifth aspect, various messages  16  evaluated by the techniques presented herein may explicitly or implicitly reference an associated message. For example, a news story may be later referenced by a message  16  updating the news story with additional information, or commenting upon the substance of the news story; and a weblog post or message  16  within a social network may explicitly or implicitly address another post or message  16  in a responsive manner. Accordingly, a message processing pipeline embodiment of the techniques presented herein may include a reference importing component, which may be configured to, for respective messages  16 , identify at least one reference to an associated message  16 , and import the at least one associated message  16  into the message  16  for the extraction of entities  42 . For example, if a message  16  posted on a web page includes a hyperlink to an associated message  16 , the content of the associated message  16  may be retrieved and included in the evaluation of the message  16  to extract entities  42  therefrom. In this manner, the entities  42  explicitly referenced by a first message  16  may be imputed to associated messages  16  explicitly or implicitly referencing the first message  16 . 
     As a fourth variation of this fifth aspect, a message  16  about a story  12  may be authored by a message source  14  in view of a particular story perspective. As a first example, a message source  14  may hold a particular political, sociological, religious, and/or philosophical perspective, and may author a commentary about a particular event, issue, or individual from that perspective. As a second example, a message  16  may be targeted to a target audience having particular demographics, such as a particular age, race, gender, ethnicity, geographic location, income bracket, educational background, literacy level, or shared interests, and the targeting may affect the story perspective of the message  16  while relating the story  12  (e.g., for a story  12  about a research study, a first message  16  targeted to the general population as a news story may have a different perspective than a second message  16  targeted to an academic community as a technical article). Accordingly, an embodiment of these techniques may include a story perspective identifying component, which may be configured to, for respective messages  16 , identify a story perspective of the message  16  with respect to the story  12 . For example, the story perspective identifying component may identify a particular vocabulary associated with each perspective (e.g., a first set of political “buzzwords” or slang often appearing in fiscally progressive messages, and a second set of political “buzzwords” or slang often appearing in fiscally conservative messages), and may identify the story perspective of a particular message  16  based on the vocabulary utilized therein. The identified story perspective may be useful, e.g., to fulfill a request  24  of a user  22  to present messages  16  relating a story  12  from a particular story perspective; e.g., a user  22  holding a particular political perspective may present a request  24  to filter the messages  16  of a message set (such as news stories of a news feed) to those authored from a story perspective. Moreover, a message processing pipeline  76  may also comprise a message source perspective identifying component, which may be configured to, for respective message sources  14  generating at least one message  16 , identify a message source perspective of the message source  14  according to the story perspectives of the stories  12  of the messages  16  generated by the message source  14 . For example, if a particular message source  14  often generates messages  16  from a particular story perspective, the message source perspective identifying component may identify the message source  14  as having a message source perspective matching the frequently identified story perspectives (e.g., a fiscally conservative message source), and this identification may later be utilized to fulfill a request  24  of a user  22  (e.g., a request  24  to filter a news feed to stories generated by news sources having a fiscally conservative perspective). 
     As a fifth variation of this fifth aspect, in addition to generating a summary  36  of a story  12  based on a set of messages  16 , it may also be advantageous to identify a “meta-summary” of the story  12 . For example, a first story  12  may relate the historical visit of U.S. President Richard Nixon to the nation of China, and a second historical story  12  may relate a historical visit by the Queen Elizabeth II to the United States. Both stories involve three entities  42 : a U.S. President and a Queen of England (both heads of state), a visit, and the nations of China and the United States (both nations of the world); thus, both stories may be summarized with the same “meta-summary” of a head of state visiting a nation of the world. It may be advantageous to associate both stories  12  with this meta-summary, e.g., in order to answer more abstract requests  24  of a user  22  (e.g., a request to present stories about heads of state visiting other nations during a particular week.) Accordingly, a message processing pipeline  76  may include a meta-summary generating component, which may be configured to, for respective stories  12 , classify respective entities  42  involved in the summary  36  (e.g., the entities  42  extracted from the messages  16  relating the story  12 ) according to an entity type, and generate a meta-summary classifying the story  12  according to the entity types of the entities  42  involved in the summary  36 . 
       FIG. 8  presents an illustration of an exemplary scenario  110  featuring the application of several variations of this fifth aspect. In this exemplary scenario  110 , a story  12  about recently released data concerning a tax cut may be related in a first message  16  posted by a first message source  14 , and also in a second message  16  posted by a second message source  14  (e.g., bloggers writing for different weblogs, or different users  22  of a social network). However, the first message source  14  may author the first message  16  from a first story perspective  114  (e.g., a fiscally progressive perspective), and the second message source  14  may author the second message  16  from a second story perspective  114  (e.g., a fiscally conservative progressive). A story perspective identifying component of a message processing pipeline  76  may evaluate the content of each message  16 , and may identify the story perspective  114  on the story  12  expressed by each message  16 . Moreover, the story perspectives  114  may be attributed to the message sources  14  of the respective messages  16 , thereby identifying each message source  14  with a particular message source perspective (particularly if the message source  14  frequently generates messages  16  from a particular story perspective  114 ). As yet another feature, the messages  16  may not explicitly identify the story  12 , but may indirectly reference the story  12  (e.g., by including a hyperlink) or by context (e.g., a reply to an earlier message  16  about the story  12 , or simply a comment that an educated human reader may interpret as a reference to the story  12 ), and a reference importing component may retrieve other messages  16  about the story  12  and include such messages  16  in the evaluation of the referring messages  16 . In this manner, the exemplary scenario  110  illustrates the application of several of the variations of this fifth aspect. Those of ordinary skill in the art may devise many such features that may be included in an embodiment of these techniques, and corresponding components that may be inserted into a message processing pipeline  76 , in accordance with the techniques presented herein. 
     A sixth aspect that may vary among embodiments of these techniques relates to the uses of the summaries  36  of stories  12  that are automatically generated by the techniques presented herein. As a first variation, the summaries  36  may simply be presented with respective stories  12  (e.g., as an automatically generated title or headline of the story  12 , or as a synopsis of the story  12 ). As a second variation, the summaries  36  may be presented with messages  16  associated with the story  12 , e.g., to supplement the message  16  with facts  34  of the story  12  that may have been omitted from the message  16 . 
     As an example of this sixth aspect, an embodiment of these techniques may store the summaries  36  of respective stories  12 , and may use the stored summaries  36  to fulfill requests  24  of users  22 . For example, a message processing pipeline  76  may include a story store, which may be configured to store, for respective stories  12 , a summary  36  and at least one message  16  associated with the story  12 . The message processing pipeline  76  may also include a story presenting component, which may be configured to, upon receiving a request  24  to present stories  12 , retrieve from the story store at least one summary  36  of at least one story  12 , and present the at least one summary  36  of the at least one story  12  in response to the request  24 . Additionally, upon receiving a selection of a selected story  12 , the story presenting component may present at least one message  16  associated with the selected story  12  in response to the request  24 . As an additional variation, the story presenting component may, for respective stories  12  to be presented in response to a request  24 , retrieve at least one media object involving at least one entity  42  involved in the story  12  (e.g., an image of an individual or location referenced in the story  12 ), and may present the at least one media object with the summary of the story  36 . 
     Additional variations of this sixth aspect may permit a selective filtering of the stories  12  so presented, based on various metadata aspects identified while processing the messages  16  associated with the stories  12 . As a first such variation, upon receiving a request  24  to present stories  12  involving a selected entity (e.g., a particular individual or location), the story presenting component may retrieve from the story store the summaries  36  of stories  12  involving the selected entity, and present the retrieved summaries  36  in response to the request  24 . As a second such variation, the story store may store, with respective messages  16 , a story perspective of the message  16  with respect to the story  12  (e.g., a political bias of respective messages  16  about a political event  12 ), and upon receiving a request  24  to present messages  16  having a particular story perspective with respect to a story  12 , the story presenting component may retrieve from the story store at least one selected message  16  having the story perspective with respect to the story  12 , and may present the at least one selected message  16  in response to the request  24 . In these and other ways, the messages  16  and stories  12  may be filtered in various ways to fulfill various requests  24  of a user  22  for a particular subset of stories  12  and messages  16 . Those of ordinary skill in the art may devise many uses of summaries  36  of stories  12  and associated metadata that may be automatically generated according to the techniques presented herein. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 
     As used in this application, the terms “component,” “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
     Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
       FIG. 9  and the following discussion provide a brief, general description of a suitable computing environment to implement embodiments of one or more of the provisions set forth herein. The operating environment of  FIG. 9  is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example computing devices include, but are not limited to, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, mini computers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     Although not required, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments. 
       FIG. 9  illustrates an example of a system  120  comprising a computing device  122  configured to implement one or more embodiments provided herein. In one configuration, computing device  122  includes at least one processing unit  126  and memory  128 . Depending on the exact configuration and type of computing device, memory  128  may be volatile (such as RAM, for example), non-volatile (such as ROM, flash memory, etc., for example) or some combination of the two. This configuration is illustrated in  FIG. 9  by dashed line  124 . 
     In other embodiments, device  122  may include additional features and/or functionality. For example, device  122  may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in  FIG. 9  by storage  130 . In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage  130 . Storage  130  may also store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in memory  128  for execution by processing unit  126 , for example. 
     The term “computer readable media” as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. Memory  128  and storage  130  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by device  122 . Any such computer storage media may be part of device  122 . 
     Device  122  may also include communication connection(s)  136  that allows device  122  to communicate with other devices. Communication connection(s)  136  may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device  122  to other computing devices. Communication connection(s)  136  may include a wired connection or a wireless connection. Communication connection(s)  136  may transmit and/or receive communication media. 
     The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     Device  122  may include input device(s)  134  such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s)  132  such as one or more displays, speakers, printers, and/or any other output device may also be included in device  122 . Input device(s)  134  and output device(s)  132  may be connected to device  122  via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s)  134  or output device(s)  132  for computing device  122 . 
     Components of computing device  122  may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device  122  may be interconnected by a network. For example, memory  128  may be comprised of multiple physical memory units located in different physical locations interconnected by a network. 
     Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device  140  accessible via network  138  may store computer readable instructions to implement one or more embodiments provided herein. Computing device  122  may access computing device  140  and download a part or all of the computer readable instructions for execution. Alternatively, computing device  122  may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device  122  and some at computing device  140 . 
     Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. 
     Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”