Capturing News Stories Once, Retelling a Thousand Ways

José Luis RedondoGarcía

EURECOMBiot, France

redondo@eurecom.fr

Giuseppe RizzoEURECOMBiot, France

giuseppe.rizzo@eurecom.fr

Raphaël TroncyEURECOMBiot, France

raphael.troncy@eurecom.fr

ABSTRACTWe live in a constantly evolving world where news storiesand relevant facts are happening every moment. For each ofthose stories, numerous news articles, posts, and social me-dia reactions are created, offering a multitude of viewpointsabout what is happening around us. Many applications havetried to deal with this complexity from very different an-gles, targeting particular needs, reconstructing certain partsof the story, and exploiting certain visualization paradigms.In this paper, we identify those challenges and study how anadequate news story representation can effectively supportthe different phases of the news consumption process. Wepropose an innovative model called News Semantic Snap-shot (NSS) that is designed to capture the entire contextof a news item. This model can feed very different applica-tions assisting the users before, during, and after the newsstory consumption. It formalizes a duality in the news an-notations that distinguishes between representative entitiesand relevant entities, and considers different relevancy di-mensions that are incorporated into the model in the formof concentric layers. Finally, we analyze the impact of thisNSS on existing prototypes and how it can support futureones.

Categories and Subject DescriptorsH.3.3 [Information Storage and Retrieval]: InformationSearch and Retrieval—retrieval models, search process

KeywordsNews Stories, News Semantic Snapshot, Entity Annotation

1. INTRODUCTIONEven the a-priori conventional stories that we daily con-

sume have some underlying facts that, during certain situa-tions and for some particular users, become important andneed to be unveiled. Those facts can be described in very

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DOI: http://dx.doi.org/10.1145/2815833.2816951.

different ways: publishers may aim to emphasize certain as-pects of the story, target a specific audience, or respond toparticular viewer’s needs. The role of the consumers can alsoevolve over the time, from a passive and less engaging be-havior to a deep-into-the-details mode that requires deeperknowledge about the facts being reported. In this highlychallenging ecosystem where stories are spread all over dif-ferent pieces of information, interpreted by many differentusers and presented by various data sources, the existenceof a model representing the entire context of the news itembecomes highly relevant.

The construction of such advanced story representationhas already been addressed in [5]. In our previous work, wemade the hypothesis that a single video news item is oftennot enough to capture the complete story being reported andcan be biased or even partially wrong. We have developedvarious information retrieval techniques for combining theoriginal news content with additional data collected fromother external sources. This process, called Named EntityExpansion, is able to produce a ranked list of named entitiesthat complements the initial set of detected entities in videosubtitles with other item-related entities captured from Webdocuments. The top n items in this list build the conceptualstructure called the Newscast Semantic Snapshot (NSS) of anews story. In [2], this NSS evolves from a plain ordered listof entities to a multi-layered concentric model, which is moreappropriate for representing the duality between the mostrepresentative entities and the other ones that are relevantto the context of the news item due to diverse reasons suchas interestingness, informativeness or popularity.

In this paper, we analyze how this NSS can support thedifferent requirements derived from the news consumptionprocess. This structure needs to (1) be easy to exploit andflexible enough for giving an answer to different applications,(2) deal with the duality present in the news annotations,by differentiating between entities that better summarize astory and the ones that acquire relevancy as the story isfurther consumed, and (3) emphasize the relationships es-tablished between the different entities inside the context ofthe story, focusing more in the reasons for having such aconnection and less in their absolute importance inside thestory. In the last part of the paper, we will analyze someprototypes that project the rich spectrum of relationshipswithin entities into a simpler and human easier way to con-sume the story, in order to understand how they can benefitfrom such a news item representation.

2. A MODEL FOR NEWS CONSUMPTION:THE NEWS SEMANTIC SNAPSHOT

The News Semantic Snapshot (NSS) is a graph structurethat tries to represent the entire context of a news story,where nodes are named entities and edges represent rela-tionships among them. According to the hypothesis statedin [2], a NSS can be modeled following a schema of concen-tric entity layers. The NSS aims to exploit and harmonize

Figure 1: Concentricity of the news item “Fugitive Edward Snow-den applies for asylum in Russia”

in a single conceptual model different semantic relationshipsestablished between the news’ entities. This model, based onthe findings made in [2], makes explicit a duality in the en-tities via two main layers namely the Core and the Crust.The former is composed of a small number of key entitiesthat are essential to identify a story. Those entities havethe highest potential to better summarize the main factsbehind the news story. They are frequently mentioned inrelated documents and therefore spottable via frequency-based functions. In Figure 1, the Core is composed of theentities Russia, Snowden, U.S., and Sheremetyevo (the air-port where the action is taking place), which are the seedsfor a good understanding of the story. On the other hand,the Crust is composed of the entities expressing the par-ticular details around the news items. They are mentionedin some specific related documents, but they are not alwaysspottable via frequency-based measures. Their relevancy isinstead grounded on the existence of relations such as pop-ularity, serendipity among those entities and the Core. InFigure 1, some entities such as Anatoli Kucherena (EdwardSnowden’s lawyer), are not so prominent at a first glance,but they definitely play a role in the story and can con-tribute to a better understanding of the facts. The semanticcontext of a news item can be therefore built by combin-ing the Core and the Crust into a single data structureNSSconcentric = Core⊕Crust. In order to go deeper in theformalization of the NSS, we focus on some other aspectsof this conceptual model that are important in the newsconsumption scenario.Reconciling Relevancy Dimensions. The concept of

relevancy is extremely wide and complex. It depends onseveral variables that two different persons that ought tojudge the relevancy of an entity would rarely agree on. How-

ever, the layer based representation used in the NSS bettersupports the complex and multi-dimensional relevancy rela-tions established among the entities involved in a news itemand allows to formalize the potential reasons that are link-ing them to the Core. The Crust becomes then a placefor hosting different relevancy dimensions [8], which bringdiversity to story description: entities denoting opinions, in-formativeness, serendipity, popularity, interestingness, un-expectedness.

Finding Predicates to Entity Relations. In [2], wediscussed the importance of discovering and explicitly es-tablishing relations among the entities inside the NSS. Wepropose now a step further by considering, not only the un-labeled relations, but also explicit predicates characterizingthe entity links. Finding such property names and formal-izing those entity dependencies is still an open challenge.In [2], co-occurrences of entities in documents collected fromthe Web revealed how tight were the relations in the contextof the story. A further analysis of those documents couldhelp to provide additional information enabling to label thepredicates.

As the NSS is a graph-based structure, this informationcan be straightforwardly incorporated into the model. Thepredicates established between the elements inside can be-come labeled links thanks to the flexible nature of the model.Prototypes can exploit such kind of annotations in order tomake the users aware of the reasons that make those entitiesrelevant.

Tracking Stories over Time. In most of the cases,the different facts, which shape the story plot, happen ina chronological order. This implies that the entities andpredicates involved in such facts are especially relevant dur-ing a particular period of time within the time span of thestory. Once more, the flexible graph-based NSS model cansupport edges annotated with temporal references in orderto reflect when a particular entity plays an important rolein the plot of the story or holds a specific relationship withothers. Tracking the evolution of those relationships in timeopens a room for timeline based summarization prototypesthat highlight the milestones characterizing the story evolu-tion.

3. THE NEWS CONSUMPTION PARADIGMPotentially, there are numerous ways of consuming a news

story StNews. Each tool or application displaying informa-tion about a news item follows a different philosophy, tar-gets a different audience, and presents the main facts froma different angle. Despite this variety of alternatives, inthis section, we propose a model for classifying those newsconsumption approaches. Having for reference the time, theuser is actually consuming the news document dnews describ-ing the story StNews, we have identified three main phases:the before, the during, and the after. In each of thosephases, a user’s behavior is different: there is an evolutionin the understanding of the story and consequently in theinformation requirements of the applications presenting thestory.

3.1 The beforeUsers, in this phase, have normally not consumed the main

news item yet, so their understanding about the story is lim-ited. Most of the times, they require a quick and easy way tointerpret the main facts so they get to know in a glance what

the news is talking about. In other cases, additional contentis displayed in order to illustrate the news context. Thistype of recommendations look for content that is very sim-ilar to dnews, leaving diversity aside. A special applicationcategory under this phase includes the advanced summariesthat aim to fully tell the story without having consumed theoriginal content.

3.2 The duringIt corresponds to the time the user is watching the main

document illustrating the story, dnews. It normally implies apassive information activity where the users are pretty muchfocused in the task of consuming the document without en-gaging in any other actions. During this phase, the user’sknowledge grows from the background information providedin the before phase to a most detailed understanding of thenews. A good example of prototypes under this categoryis a second screen application aiming to illustrate what isbeing said on the news with minimal user interaction.

3.3 The afterThe user became fully aware of the basics of the story

and wants to go deeper into the details, switching to an ac-tive mode: browsing description of entities categorized intodimensions or ultimately jumping to other related stories.The level of interaction drastically increases since the userbecomes more engaged, moved by the curiosity of discover-ing more details. The main document dnews can be enrichedwith additional content, focusing on diversity, and detailingsome specific facts of StNews. Other applications falling un-der the same consumption phase are advanced interactivesummaries with browsing capabilities.

3.4 The NNS in the Consumption ProcessWe formulate, as hypothesis, that the NSS of a news item

is a knowledge representation model that effectively capturesthe context of a story and can support different existingnews applications. This graph layer-based structure helpsto populate very diverse prototypes aiming to support usersin interpreting the news. For the sake of illustrating our hy-pothesis and without claiming to provide an exhaustive plotbased on quantitative data, Figure 2 shows how the dualitybetween Core and Crust in the concentric model can bet-ter satisfy the evolution of user consumption needs acrossthe different consumption phases, as a result of changes inaspects like viewers’ knowledge about the story, user’s en-gagement and content diversity.

As defined in the Formula 1, Section 4 in [2], entities in theCore usually express the most general, upper-level conceptsthat drive the story behind the news item. Even if thoseentities will be present in almost every stage of the newsconsumption, they have a stronger decisive role in the beforephase (left side of Figure 2), decreasing in importance asdnews is consumed and the after visualizations come intoplay.

The during is a special phase that requires both Coreand Crust entities (middle part of Figure 2), specially whenthey are mentioned in the document dnews. In particular,Core entities start to be less demanded since they have of-ten been consumed during the before, while Crust entitiesstart bringing added value in revealing the non-obvious factsaround the story plot.

In the last phase of the consumption process, the so-called

Figure 2: Core and Crust usage along the different consumptionphases

after, users have already a fair understanding of the newsstory. The entities in the Core, which were highly presentduring the previous phases, become often too obvious andare therefore not so critical to be used. Instead, the entitiesin the Crust bring those particular details that users want toconsume, in an attempt to move from a general understand-ing of the news item to explore specific story details (rightside of Figure 2). Since the Crust considers different rele-vancy dimensions, applications can easily move along themand bring the diversity desired at those latest stages of theconsumption process. In addition, for those applications dis-playing timeline based summaries, we propose an additionalhypothesis stating that the Core remains stable in time andhave less interest, while the Crust contains the entities thatbring the stand-out information in particular periods of timeand need to be displayed.

4. AN ECOSYSTEM OF NEWS APPLICA-TIONS

In this section, we review existing applications and pro-totypes for consuming news. We classify them according tothe different consumption phases identified in Section 3, andwe analyze how they would benefit from a graph represen-tation model like the News Semantic Snapshot in order tomake a first qualitative evaluation of our hypothesis.

Prototypes for Before Consuming the News Item.In [6], we presented an approach for getting a quick overviewof a video content enabling the user to decide if he is inter-ested or not in the story. We automatically select somefragments within the video called Hotspots, which containannotations with higher frequency scores. This notion ofrepresentativeness is clearly aligned with the definition ofthe Core. Entities inside this layer could be straightfor-wardly used for the Hotspots creation.

Something similar occurs in [1] where some small videofragments are hyperlinked to others based on some visual ortopical similarity. Even if such a task can be tackled usingmultimodal analysis techniques, applications at early stagesof the news consumption focus mostly in finding contentas similar as possible to the original. Therefore, entities inthe Core are suitable for triggering searches on document

indexes where that additional content can be found.Prototypes During the Consumption of the News

Item. In [4], we presented a second screen application im-plementing a slideshow that gives the user access to factualinformation about Person-type, Location-type and Organization-type entities that are related to a news story displayed in themain screen. Core-like entities are still shown when men-tioned in the video to illustrate the story as a whole, butthere is an increasing use of other entities clarifying morespecific facts in the video as they are displayed (Crust-likeentities). Given the absence of a NSS in [4] to feed such pro-totype, we developed a first implementation of the EntityExpansion Algorithm [5] in order to recreate a very basicversion of the Crust.

Prototypes for After Consuming the News Item.We find an example of an application illustrating the storyafter consuming the news item in [5]. The active mode ofthe demo targeted the idea of a user who wants to furtherdig into the details of the story via some additional contentthat is proposed along different dimensions. Some of thoseadditional content facets can easily match the layers envi-sioned in the Crust definition, like Opinions from Expertsthat aligns to opinions, or In Other Sources that aligns toinformativeness, revealing the importance of the multi-layerphilosophy inside the News Semantic Snapshot.

We can also include applications that offer advanced in-teractive visualizations for summarizing the entire contextof the story, an extremely challenging task, even when per-formed by experts in the domain. The representation modeloffered by the NSS considers relations between entities thatcan help to implement conceptual diagrams where entitiesare related to each other via particular connections that canbe visualized like in [7].

A third example in this category are the time-based rep-resentations that break down the story in relevant facts thatare chronologically represented over the X axis. In [3], weanalyzed the story of the Italian Elections 2013 during oneweek after the voting process. One of the main difficul-ties encountered during its implementation was to filter outthose entities that were buzzing during the entire week sothey became obvious like Italy, and to promote instead thoseentities that peaked in relevance during certain moments ofthe week for particular reasons, like Merkel who had a meet-ing with the Italian president on the 1st of March. Thisphenomena reinforces our hypothesis about the Core enti-ties remaining stable in time and becoming useless for suchtimelines prototypes, while the ones in the Crust bringingthe interesting facts that need to be shown.

Other Prototypes. In addition to the previous proto-types, there are also other applications targeting a biggerportion of the news consumption phases spectrum. A goodexample can be found in the online demo Hyperted1 that of-fers an innovative way to consume TED talks, by supportingthe user not only at the pre-consumption stage via Hotspotscalculation, but also the during the viewing through entityhighlighting and in the period just after by linking to similarcourses and other related TED chapters.

5. CONCLUSIONThe different applications consuming news can benefit

from the existence of a graph-based model able to capture

1http://linkedtv.eurecom.fr/Hyperted/

the entire context of the story. We proposed a model calledthe News Semantic Snapshot that is grounded on the ex-istence of semantic relations between entities describing anews story. This model has a concentric nature consider-ing two main layers: the Core, which includes the mostrepresentative and frequent entities, and the Crust whichis composed of additional entities that become relevant be-cause of certain relationships happening between them andthe Core. In addition, we have identified three phases of thenews consumption process: the before, the during, and theafter. The NSS can support applications falling inside eachof those categories. As support of the underlined hypothesis,we have reviewed some of our past prototypes, identifyingthe challenges we faced when capturing the story informa-tion, and explaining how the use of NSS to feed their needswould have led to a better implementation.

In future work, we plan to experiment with new tech-niques for automatically populating the model. We aimto use existing domain specific knowledge bases for furthercontextualizing the relationships present in the NSS, andexhaustively evaluate what is the relevancy of the variousdimensions that composed the Crust so that users get thebest consuming experience possible.

AcknowledgmentsThis work has been partially supported by Bpifrance withinthe NexGen-TV Project, under grant number F1504054U,and by the European Union’s 7th Framework Programmevia the project LinkedTV (GA 287911).

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