Introduction
Wikipedia is the largest source of open and collaboratively curated knowledge in the world. Introduced in 2001, it has evolved into a reference work with around 5m pages for the English Wikipedia alone. In addition, entities and event pages are updated quickly via collaborative editing and all edits are encouraged to include source citations, creating a knowledge base which aims at being both timely as well as authoritative. As a result, it has become the preferred source of information consumption about entities and events. Moreso, this knowledge is harvested and utilized in building knowledge bases like YAGO BIBREF0 and DBpedia BIBREF1 , and used in applications like text categorization BIBREF2 , entity disambiguation BIBREF3 , entity ranking BIBREF4 and distant supervision BIBREF5 , BIBREF6 .
However, not all Wikipedia pages referring to entities (entity pages) are comprehensive: relevant information can either be missing or added with a delay. Consider the city of New Orleans and the state of Odisha which were severely affected by cyclones Hurricane Katrina and Odisha Cyclone, respectively. While Katrina finds extensive mention in the entity page for New Orleans, Odisha Cyclone which has 5 times more human casualties (cf. Figure FIGREF2 ) is not mentioned in the page for Odisha. Arguably Katrina and New Orleans are more popular entities, but Odisha Cyclone was also reported extensively in national and international news outlets. This highlights the lack of important facts in trunk and long-tail entity pages, even in the presence of relevant sources. In addition, previous studies have shown that there is an inherent delay or lag when facts are added to entity pages BIBREF7 .
To remedy these problems, it is important to identify information sources that contain novel and salient facts to a given entity page. However, not all information sources are equal. The online presence of major news outlets is an authoritative source due to active editorial control and their articles are also a timely container of facts. In addition, their use is in line with current Wikipedia editing practice, as is shown in BIBREF7 that almost 20% of current citations in all entity pages are news articles. We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative.
Existing efforts to populate Wikipedia BIBREF8 start from an entity page and then generate candidate documents about this entity using an external search engine (and then post-process them). However, such an approach lacks in (a) reproducibility since rankings vary with time with obvious bias to recent news (b) maintainability since document acquisition for each entity has to be periodically performed. To this effect, our news suggestion considers a news article as input, and determines if it is valuable for Wikipedia. Specifically, given an input news article INLINEFORM0 and a state of Wikipedia, the news suggestion problem identifies the entities mentioned in INLINEFORM1 whose entity pages can improve upon suggesting INLINEFORM2 . Most of the works on knowledge base acceleration BIBREF9 , BIBREF10 , BIBREF11 , or Wikipedia page generation BIBREF8 rely on high quality input sources which are then utilized to extract textual facts for Wikipedia page population. In this work, we do not suggest snippets or paraphrases but rather entire articles which have a high potential importance for entity pages. These suggested news articles could be consequently used for extraction, summarization or population either manually or automatically – all of which rely on high quality and relevant input sources.
We identify four properties of good news recommendations: salience, relative authority, novelty and placement. First, we need to identify the most salient entities in a news article. This is done to avoid pollution of entity pages with only marginally related news. Second, we need to determine whether the news is important to the entity as only the most relevant news should be added to a precise reference work. To do this, we compute the relative authority of all entities in the news article: we call an entity more authoritative than another if it is more popular or noteworthy in the real world. Entities with very high authority have many news items associated with them and only the most relevant of these should be included in Wikipedia whereas for entities of lower authority the threshold for inclusion of a news article will be lower. Third, a good recommendation should be able to identify novel news by minimizing redundancy coming from multiple news articles. Finally, addition of facts is facilitated if the recommendations are fine-grained, i.e., recommendations are made on the section level rather than the page level (placement).
Approach and Contributions. We propose a two-stage news suggestion approach to entity pages. In the first stage, we determine whether a news article should be suggested for an entity, based on the entity's salience in the news article, its relative authority and the novelty of the article to the entity page. The second stage takes into account the class of the entity for which the news is suggested and constructs section templates from entities of the same class. The generation of such templates has the advantage of suggesting and expanding entity pages that do not have a complete section structure in Wikipedia, explicitly addressing long-tail and trunk entities. Afterwards, based on the constructed template our method determines the best fit for the news article with one of the sections.
We evaluate the proposed approach on a news corpus consisting of 351,982 articles crawled from the news external references in Wikipedia from 73,734 entity pages. Given the Wikipedia snapshot at a given year (in our case [2009-2014]), we suggest news articles that might be cited in the coming years. The existing news references in the entity pages along with their reference date act as our ground-truth to evaluate our approach. In summary, we make the following contributions.
Related Work
As we suggest a new problem there is no current work addressing exactly the same task. However, our task has similarities to Wikipedia page generation and knowledge base acceleration. In addition, we take inspiration from Natural Language Processing (NLP) methods for salience detection.
Wikipedia Page Generation is the problem of populating Wikipedia pages with content coming from external sources. Sauper and Barzilay BIBREF8 propose an approach for automatically generating whole entity pages for specific entity classes. The approach is trained on already-populated entity pages of a given class (e.g. `Diseases') by learning templates about the entity page structure (e.g. diseases have a treatment section). For a new entity page, first, they extract documents via Web search using the entity title and the section title as a query, for example `Lung Cancer'+`Treatment'. As already discussed in the introduction, this has problems with reproducibility and maintainability. However, their main focus is on identifying the best paragraphs extracted from the collected documents. They rank the paragraphs via an optimized supervised perceptron model for finding the most representative paragraph that is the least similar to paragraphs in other sections. This paragraph is then included in the newly generated entity page. Taneva and Weikum BIBREF12 propose an approach that constructs short summaries for the long tail. The summaries are called `gems' and the size of a `gem' can be user defined. They focus on generating summaries that are novel and diverse. However, they do not consider any structure of entities, which is present in Wikipedia.
In contrast to BIBREF8 and BIBREF12 , we actually focus on suggesting entire documents to Wikipedia entity pages. These are authoritative documents (news), which are highly relevant for the entity, novel for the entity and in which the entity is salient. Whereas relevance in Sauper and Barzilay is implicitly computed by web page ranking we solve that problem by looking at relative authority and salience of an entity, using the news article and entity page only. As Sauper and Barzilay concentrate on empty entity pages, the problem of novelty of their content is not an issue in their work whereas it is in our case which focuses more on updating entities. Updating entities will be more and more important the bigger an existing reference work is. Both the approaches in BIBREF8 and BIBREF12 (finding paragraphs and summarization) could then be used to process the documents we suggest further. Our concentration on news is also novel.
Knowledge Base Acceleration. In this task, given specific information extraction templates, a given corpus is analyzed in order to find worthwhile mentions of an entity or snippets that match the templates. Balog BIBREF9 , BIBREF10 recommend news citations for an entity. Prior to that, the news articles are classified for their appropriateness for an entity, where as features for the classification task they use entity, document, entity-document and temporal features. The best performing features are those that measure similarity between an entity and the news document. West et al. BIBREF13 consider the problem of knowledge base completion, through question answering and complete missing facts in Freebase based on templates, i.e. Frank_Zappa bornIn Baltymore, Maryland.
In contrast, we do not extract facts for pre-defined templates but rather suggest news articles based on their relevance to an entity. In cases of long-tail entities, we can suggest to add a novel section through our abstraction and generation of section templates at entity class level.
Entity Salience. Determining which entities are prominent or salient in a given text has a long history in NLP, sparked by the linguistic theory of Centering BIBREF14 . Salience has been used in pronoun and co-reference resolution BIBREF15 , or to predict which entities will be included in an abstract of an article BIBREF11 . Frequent features to measure salience include the frequency of an entity in a document, positioning of an entity, grammatical function or internal entity structure (POS tags, head nouns etc.). These approaches are not currently aimed at knowledge base generation or Wikipedia coverage extension but we postulate that an entity's salience in a news article is a prerequisite to the news article being relevant enough to be included in an entity page. We therefore use the salience features in BIBREF11 as part of our model. However, these features are document-internal — we will show that they are not sufficient to predict news inclusion into an entity page and add features of entity authority, news authority and novelty that measure the relations between several entities, between entity and news article as well as between several competing news articles.
Terminology and Problem Definition
We are interested in named entities mentioned in documents. An entity INLINEFORM0 can be identified by a canonical name, and can be mentioned differently in text via different surface forms. We canonicalize these mentions to entity pages in Wikipedia, a method typically known as entity linking. We denote the set of canonicalized entities extracted and linked from a news article INLINEFORM1 as INLINEFORM2 . For example, in Figure FIGREF7 , entities are canonicalized into Wikipedia entity pages (e.g. Odisha is canonicalized to the corresponding article). For a collection of news articles INLINEFORM3 , we further denote the resulting set of entities by INLINEFORM4 .
Information in an entity page is organized into sections and evolves with time as more content is added. We refer to the state of Wikipedia at a time INLINEFORM0 as INLINEFORM1 and the set of sections for an entity page INLINEFORM2 as its entity profile INLINEFORM3 . Unlike news articles, text in Wikipedia could be explicitly linked to entity pages through anchors. The set of entities explicitly referred in text from section INLINEFORM4 is defined as INLINEFORM5 . Furthermore, Wikipedia induces a category structure over its entities, which is exploited by knowledge bases like YAGO (e.g. Barack_Obama isA Person). Consequently, each entity page belongs to one or more entity categories or classes INLINEFORM6 . Now we can define our news suggestion problem below:
Definition 1 (News Suggestion Problem) Given a set of news articles INLINEFORM0 and set of Wikipedia entity pages INLINEFORM1 (from INLINEFORM2 ) we intend to suggest a news article INLINEFORM3 published at time INLINEFORM4 to entity page INLINEFORM5 and additionally to the most relevant section for the entity page INLINEFORM6 .
Approach Overview
We approach the news suggestion problem by decomposing it into two tasks:
AEP: Article–Entity placement
ASP: Article–Section placement
In this first step, for a given entity-news pair INLINEFORM0 , we determine whether the given news article INLINEFORM1 should be suggested (we will refer to this as `relevant') to entity INLINEFORM2 . To generate such INLINEFORM3 pairs, we perform the entity linking process, INLINEFORM4 , for INLINEFORM5 .
The article–entity placement task (described in detail in Section SECREF16 ) for a pair INLINEFORM0 outputs a binary label (either `non-relevant' or `relevant') and is formalized in Equation EQREF14 . DISPLAYFORM0
In the second step, we take into account all `relevant' pairs INLINEFORM0 and find the correct section for article INLINEFORM1 in entity INLINEFORM2 , respectively its profile INLINEFORM3 (see Section SECREF30 ). The article–section placement task, determines the correct section for the triple INLINEFORM4 , and is formalized in Equation EQREF15 . DISPLAYFORM0
In the subsequent sections we describe in details how we approach the two tasks for suggesting news articles to entity pages.
News Article Suggestion
In this section, we provide an overview of the news suggestion approach to Wikipedia entity pages (see Figure FIGREF7 ). The approach is split into two tasks: (i) article-entity (AEP) and (ii) article-section (ASP) placement. For a Wikipedia snapshot INLINEFORM0 and a news corpus INLINEFORM1 , we first determine which news articles should be suggested to an entity INLINEFORM2 . We will denote our approach for AEP by INLINEFORM3 . Finally, we determine the most appropriate section for the ASP task and we denote our approach with INLINEFORM4 .
In the following, we describe the process of learning the functions INLINEFORM0 and INLINEFORM1 . We introduce features for the learning process, which encode information regarding the entity salience, relative authority and novelty in the case of AEP task. For the ASP task, we measure the overall fit of an article to the entity sections, with the entity being an input from AEP task. Additionally, considering that the entity profiles INLINEFORM2 are incomplete, in the case of a missing section we suggest and expand the entity profiles based on section templates generated from entities of the same class INLINEFORM3 (see Section UID34 ).
Article–Entity Placement
In this step we learn the function INLINEFORM0 to correctly determine whether INLINEFORM1 should be suggested for INLINEFORM2 , basically a binary classification model (0=`non-relevant' and 1=`relevant'). Note that we are mainly interested in finding the relevant pairs in this task. For every news article, the number of disambiguated entities is around 30 (but INLINEFORM3 is suggested for only two of them on average). Therefore, the distribution of `non-relevant' and `relevant' pairs is skewed towards the earlier, and by simply choosing the `non-relevant' label we can achieve a high accuracy for INLINEFORM4 . Finding the relevant pairs is therefore a considerable challenge.
An article INLINEFORM0 is suggested to INLINEFORM1 by our function INLINEFORM2 if it fulfills the following properties. The entity INLINEFORM3 is salient in INLINEFORM4 (a central concept), therefore ensuring that INLINEFORM5 is about INLINEFORM6 and that INLINEFORM7 is important for INLINEFORM8 . Next, given the fact there might be many articles in which INLINEFORM9 is salient, we also look at the reverse property, namely whether INLINEFORM10 is important for INLINEFORM11 . We do this by comparing the authority of INLINEFORM12 (which is a measure of popularity of an entity, such as its frequency of mention in a whole corpus) with the authority of its co-occurring entities in INLINEFORM13 , leading to a feature we call relative authority. The intuition is that for an entity that has overall lower authority than its co-occurring entities, a news article is more easily of importance. Finally, if the article we are about to suggest is already covered in the entity profile INLINEFORM14 , we do not wish to suggest redundant information, hence the novelty. Therefore, the learning objective of INLINEFORM15 should fulfill the following properties. Table TABREF21 shows a summary of the computed features for INLINEFORM16 .
Salience: entity INLINEFORM0 should be a salient entity in news article INLINEFORM1
Relative Authority: the set of entities INLINEFORM0 with which INLINEFORM1 co-occurs should have higher authority than INLINEFORM2 , making INLINEFORM3 important for INLINEFORM4
Novelty: news article INLINEFORM0 should provide novel information for entity INLINEFORM1 taking into account its profile INLINEFORM2
Baseline Features. As discussed in Section SECREF2 , a variety of features that measure salience of an entity in text are available from the NLP community. We reimplemented the ones in Dunietz and Gillick BIBREF11 . This includes a variety of features, e.g. positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in. Table 2 in BIBREF11 gives details.
Relative Entity Frequency. Although frequency of mention and positional features play some role in baseline features, their interaction is not modeled by a single feature nor do the positional features encode more than sentence position. We therefore suggest a novel feature called relative entity frequency, INLINEFORM0 , that has three properties.: (i) It rewards entities for occurring throughout the text instead of only in some parts of the text, measured by the number of paragraphs it occurs in (ii) it rewards entities that occur more frequently in the opening paragraphs of an article as we model INLINEFORM1 as an exponential decay function. The decay corresponds to the positional index of the news paragraph. This is inspired by the news-specific discourse structure that tends to give short summaries of the most important facts and entities in the opening paragraphs. (iii) it compares entity frequency to the frequency of its co-occurring mentions as the weight of an entity appearing in a specific paragraph, normalized by the sum of the frequencies of other entities in INLINEFORM2 . DISPLAYFORM0
where, INLINEFORM0 represents a news paragraph from INLINEFORM1 , and with INLINEFORM2 we indicate the set of all paragraphs in INLINEFORM3 . The frequency of INLINEFORM4 in a paragraph INLINEFORM5 is denoted by INLINEFORM6 . With INLINEFORM7 and INLINEFORM8 we indicate the number of paragraphs in which entity INLINEFORM9 occurs, and the total number of paragraphs, respectively.
Relative Authority. In this case, we consider the comparative relevance of the news article to the different entities occurring in it. As an example, let us consider the meeting of the Sudanese bishop Elias Taban with Hillary Clinton. Both entities are salient for the meeting. However, in Taban's Wikipedia page, this meeting is discussed prominently with a corresponding news reference, whereas in Hillary Clinton's Wikipedia page it is not reported at all. We believe this is not just an omission in Clinton's page but mirrors the fact that for the lesser known Taban the meeting is big news whereas for the more famous Clinton these kind of meetings are a regular occurrence, not all of which can be reported in what is supposed to be a selection of the most important events for her. Therefore, if two entities co-occur, the news is more relevant for the entity with the lower a priori authority.
The a priori authority of an entity (denoted by INLINEFORM0 ) can be measured in several ways. We opt for two approaches: (i) probability of entity INLINEFORM1 occurring in the corpus INLINEFORM2 , and (ii) authority assessed through centrality measures like PageRank BIBREF16 . For the second case we construct the graph INLINEFORM3 consisting of entities in INLINEFORM4 and news articles in INLINEFORM5 as vertices. The edges are established between INLINEFORM6 and entities in INLINEFORM7 , that is INLINEFORM8 , and the out-links from INLINEFORM9 , that is INLINEFORM10 (arrows present the edge direction).
Starting from a priori authority, we proceed to relative authority by comparing the a priori authority of co-occurring entities in INLINEFORM0 . We define the relative authority of INLINEFORM1 as the proportion of co-occurring entities INLINEFORM2 that have a higher a priori authority than INLINEFORM3 (see Equation EQREF28 . DISPLAYFORM0
As we might run the danger of not suggesting any news articles for entities with very high a priori authority (such as Clinton) due to the strict inequality constraint, we can relax the constraint such that the authority of co-occurring entities is above a certain threshold.
News Domain Authority. The news domain authority addresses two main aspects. Firstly, if bundled together with the relative authority feature, we can ensure that dependent on the entity authority, we suggest news from authoritative sources, hence ensuring the quality of suggested articles. The second aspect is in a news streaming scenario where multiple news domains report the same event — ideally only articles coming from authoritative sources would fulfill the conditions for the news suggestion task.
The news domain authority is computed based on the number of news references in Wikipedia coming from a particular news domain INLINEFORM0 . This represents a simple prior that a news article INLINEFORM1 is from domain INLINEFORM2 in corpus INLINEFORM3 . We extract the domains by taking the base URLs from the news article URLs.
An important feature when suggesting an article INLINEFORM0 to an entity INLINEFORM1 is the novelty of INLINEFORM2 w.r.t the already existing entity profile INLINEFORM3 . Studies BIBREF17 have shown that on comparable collections to ours (TREC GOV2) the number of duplicates can go up to INLINEFORM4 . This figure is likely higher for major events concerning highly authoritative entities on which all news media will report.
Given an entity INLINEFORM0 and the already added news references INLINEFORM1 up to year INLINEFORM2 , the novelty of INLINEFORM3 at year INLINEFORM4 is measured by the KL divergence between the language model of INLINEFORM5 and articles in INLINEFORM6 . We combine this measure with the entity overlap of INLINEFORM7 and INLINEFORM8 . The novelty value of INLINEFORM9 is given by the minimal divergence value. Low scores indicate low novelty for the entity profile INLINEFORM10 .
N(n|e) = n'Nt-1{DKL((n') || (n)) + DKL((N) || (n)).
DKL((n') || (n)). (1-) jaccard((n'),(n))} where INLINEFORM0 is the KL divergence of the language models ( INLINEFORM1 and INLINEFORM2 ), whereas INLINEFORM3 is the mixing weight ( INLINEFORM4 ) between the language models INLINEFORM5 and the entity overlap in INLINEFORM6 and INLINEFORM7 .
Here we introduce the evaluation setup and analyze the results for the article–entity (AEP) placement task. We only report the evaluation metrics for the `relevant' news-entity pairs. A detailed explanation on why we focus on the `relevant' pairs is provided in Section SECREF16 .
Baselines. We consider the following baselines for this task.
B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 .
B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 .
Learning Models. We use Random Forests (RF) BIBREF23 . We learn the RF on all computed features in Table TABREF21 . The optimization on RF is done by splitting the feature space into multiple trees that are considered as ensemble classifiers. Consequently, for each classifier it computes the margin function as a measure of the average count of predicting the correct class in contrast to any other class. The higher the margin score the more robust the model.
Metrics. We compute precision P, recall R and F1 score for the relevant class. For example, precision is the number of news-entity pairs we correctly labeled as relevant compared to our ground truth divided by the number of all news-entity pairs we labeled as relevant.
The following results measure the effectiveness of our approach in three main aspects: (i) overall performance of INLINEFORM0 and comparison to baselines, (ii) robustness across the years, and (iii) optimal model for the AEP placement task.
Performance. Figure FIGREF55 shows the results for the years 2009 and 2013, where we optimized the learning objective with instances from year INLINEFORM0 and evaluate on the years INLINEFORM1 (see Section SECREF46 ). The results show the precision–recall curve. The red curve shows baseline B1 BIBREF11 , and the blue one shows the performance of INLINEFORM2 . The curve shows for varying confidence scores (high to low) the precision on labeling the pair INLINEFORM3 as `relevant'. In addition, at each confidence score we can compute the corresponding recall for the `relevant' label. For high confidence scores on labeling the news-entity pairs, the baseline B1 achieves on average a precision score of P=0.50, while INLINEFORM4 has P=0.93. We note that with the drop in the confidence score the corresponding precision and recall values drop too, and the overall F1 score for B1 is around F1=0.2, in contrast we achieve an average score of F1=0.67.
It is evident from Figure FIGREF55 that for the years 2009 and 2013, INLINEFORM0 significantly outperforms the baseline B1. We measure the significance through the t-test statistic and get a p-value of INLINEFORM1 . The improvement we achieve over B1 in absolute numbers, INLINEFORM2 P=+0.5 in terms of precision for the years between 2009 and 2014, and a similar improvement in terms of F1 score. The improvement for recall is INLINEFORM3 R=+0.4. The relative improvement over B1 for P and F1 is almost 1.8 times better, while for recall we are 3.5 times better. In Table TABREF58 we show the overall scores for the evaluation metrics for B1 and INLINEFORM4 . Finally, for B2 we achieve much poorer performance, with average scores of P=0.21, R=0.20 and F1=0.21.
Robustness. In Table TABREF58 , we show the overall performance for the years between 2009 and 2013. An interesting observation we make is that we have a very robust performance and the results are stable across the years. If we consider the experimental setup, where for year INLINEFORM0 we optimize the learning objective with only 74k training instances and evaluate on the rest of the instances, it achieves a very good performance. We predict with F1=0.68 the remaining 469k instances for the years INLINEFORM1 .
The results are particularly promising considering the fact that the distribution between our two classes is highly skewed. On average the number of `relevant' pairs account for only around INLINEFORM0 of all pairs. A good indicator to support such a statement is the kappa (denoted by INLINEFORM1 ) statistic. INLINEFORM2 measures agreement between the algorithm and the gold standard on both labels while correcting for chance agreement (often expected due to extreme distributions). The INLINEFORM3 scores for B1 across the years is on average INLINEFORM4 , while for INLINEFORM5 we achieve a score of INLINEFORM6 (the maximum score for INLINEFORM7 is 1).
In Figure FIGREF60 we show the impact of the individual feature groups that contribute to the superior performance in comparison to the baselines. Relative entity frequency from the salience feature, models the entity salience as an exponentially decaying function based on the positional index of the paragraph where the entity appears. The performance of INLINEFORM0 with relative entity frequency from the salience feature group is close to that of all the features combined. The authority and novelty features account to a further improvement in terms of precision, by adding roughly a 7%-10% increase. However, if both feature groups are considered separately, they significantly outperform the baseline B1.
Article–Section Placement
We model the ASP placement task as a successor of the AEP task. For all the `relevant' news entity pairs, the task is to determine the correct entity section. Each section in a Wikipedia entity page represents a different topic. For example, Barack Obama has the sections `Early Life', `Presidency', `Family and Personal Life' etc. However, many entity pages have an incomplete section structure. Incomplete or missing sections are due to two Wikipedia properties. First, long-tail entities miss information and sections due to their lack of popularity. Second, for all entities whether popular or not, certain sections might occur for the first time due to real world developments. As an example, the entity Germanwings did not have an `Accidents' section before this year's disaster, which was the first in the history of the airline.
Even if sections are missing for certain entities, similar sections usually occur in other entities of the same class (e.g. other airlines had disasters and therefore their pages have an accidents section). We exploit such homogeneity of section structure and construct templates that we use to expand entity profiles. The learning objective for INLINEFORM0 takes into account the following properties:
Section-templates: account for incomplete section structure for an entity profile INLINEFORM0 by constructing section templates INLINEFORM1 from an entity class INLINEFORM2
Overall fit: measures the overall fit of a news article to sections in the section templates INLINEFORM0
Given the fact that entity profiles are often incomplete, we construct section templates for every entity class. We group entities based on their class INLINEFORM0 and construct section templates INLINEFORM1 . For different entity classes, e.g. Person and Location, the section structure and the information represented in those section varies heavily. Therefore, the section templates are with respect to the individual classes in our experimental setup (see Figure FIGREF42 ). DISPLAYFORM0
Generating section templates has two main advantages. Firstly, by considering class-based profiles, we can overcome the problem of incomplete individual entity profiles and thereby are able to suggest news articles to sections that do not yet exist in a specific entity INLINEFORM0 . The second advantage is that we are able to canonicalize the sections, i.e. `Early Life' and `Early Life and Childhood' would be treated similarly.
To generate the section template INLINEFORM0 , we extract all sections from entities of a given type INLINEFORM1 at year INLINEFORM2 . Next, we cluster the entity sections, based on an extended version of k–means clustering BIBREF18 , namely x–means clustering introduced in Pelleg et al. which estimates the number of clusters efficiently BIBREF19 . As a similarity metric we use the cosine similarity computed based on the tf–idf models of the sections. Using the x–means algorithm we overcome the requirement to provide the number of clusters k beforehand. x–means extends the k–means algorithm, such that a user only specifies a range [ INLINEFORM3 , INLINEFORM4 ] that the number of clusters may reasonably lie in.
The learning objective of INLINEFORM0 is to determine the overall fit of a news article INLINEFORM1 to one of the sections in a given section template INLINEFORM2 . The template is pre-determined by the class of the entity for which the news is suggested as relevant by INLINEFORM3 . In all cases, we measure how well INLINEFORM4 fits each of the sections INLINEFORM5 as well as the specific entity section INLINEFORM6 . The section profiles in INLINEFORM7 represent the aggregated entity profiles from all entities of class INLINEFORM8 at year INLINEFORM9 .
To learn INLINEFORM0 we rely on a variety of features that consider several similarity aspects as shown in Table TABREF31 . For the sake of simplicity we do not make the distinction in Table TABREF31 between the individual entity section and class-based section similarities, INLINEFORM1 and INLINEFORM2 , respectively. Bear in mind that an entity section INLINEFORM3 might be present at year INLINEFORM4 but not at year INLINEFORM5 (see for more details the discussion on entity profile expansion in Section UID69 ).
Topic. We use topic similarities to ensure (i) that the content of INLINEFORM0 fits topic-wise with a specific section text and (ii) that it has a similar topic to previously referred news articles in that section. In a pre-processing stage we compute the topic models for the news articles, entity sections INLINEFORM1 and the aggregated class-based sections in INLINEFORM2 . The topic models are computed using LDA BIBREF20 . We only computed a single topic per article/section as we are only interested in topic term overlaps between article and sections. We distinguish two main features: the first feature measures the overlap of topic terms between INLINEFORM3 and the entity section INLINEFORM4 and INLINEFORM5 , and the second feature measures the overlap of the topic model of INLINEFORM6 against referred news articles in INLINEFORM7 at time INLINEFORM8 .
Syntactic. These features represent a mechanism for conveying the importance of a specific text snippet, solely based on the frequency of specific POS tags (i.e. NNP, CD etc.), as commonly used in text summarization tasks. Following the same intuition as in BIBREF8 , we weigh the importance of articles by the count of specific POS tags. We expect that for different sections, the importance of POS tags will vary. We measure the similarity of POS tags in a news article against the section text. Additionally, we consider bi-gram and tri-gram POS tag overlap. This exploits similarity in syntactical patterns between the news and section text.
Lexical. As lexical features, we measure the similarity of INLINEFORM0 against the entity section text INLINEFORM1 and the aggregate section text INLINEFORM2 . Further, we distinguish between the overall similarity of INLINEFORM3 and that of the different news paragraphs ( INLINEFORM4 which denotes the paragraphs of INLINEFORM5 up to the 5th paragraph). A higher similarity on the first paragraphs represents a more confident indicator that INLINEFORM6 should be suggested to a specific section INLINEFORM7 . We measure the similarity based on two metrics: (i) the KL-divergence between the computed language models and (ii) cosine similarity of the corresponding paragraph text INLINEFORM8 and section text.
Entity-based. Another feature set we consider is the overlap of named entities and their corresponding entity classes. For different entity sections, we expect to find a particular set of entity classes that will correlate with the section, e.g. `Early Life' contains mostly entities related to family, school, universities etc.
Frequency. Finally, we gather statistics about the number of entities, paragraphs, news article length, top– INLINEFORM0 entities and entity classes, and the frequency of different POS tags. Here we try to capture patterns of articles that are usually cited in specific sections.
Evaluation Plan
In this section we outline the evaluation plan to verify the effectiveness of our learning approaches. To evaluate the news suggestion problem we are faced with two challenges.
What comprises the ground truth for such a task ?
How do we construct training and test splits given that entity pages consists of text added at different points in time ?
Consider the ground truth challenge. Evaluating if an arbitrary news article should be included in Wikipedia is both subjective and difficult for a human if she is not an expert. An invasive approach, which was proposed by Barzilay and Sauper BIBREF8 , adds content directly to Wikipedia and expects the editors or other users to redact irrelevant content over a period of time. The limitations of such an evaluation technique is that content added to long-tail entities might not be evaluated by informed users or editors in the experiment time frame. It is hard to estimate how much time the added content should be left on the entity page. A more non-invasive approach could involve crowdsourcing of entity and news article pairs in an IR style relevance assessment setup. The problem of such an approach is again finding knowledgeable users or experts for long-tail entities. Thus the notion of relevance of a news recommendation is challenging to evaluate in a crowd setup.
We take a slightly different approach by making an assumption that the news articles already present in Wikipedia entity pages are relevant. To this extent, we extract a dataset comprising of all news articles referenced in entity pages (details in Section SECREF40 ). At the expense of not evaluating the space comprising of news articles absent in Wikipedia, we succeed in (i) avoiding restrictive assumptions about the quality of human judgments, (ii) being invasive and polluting Wikipedia, and (iii) deriving a reusable test bed for quicker experimentation.
The second challenge of construction of training and test set separation is slightly easier and is addressed in Section SECREF46 .
Datasets
The datasets we use for our experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history. The generated data represents one of the contributions of our paper. The datasets are the following:
Entity Classes. We focus on a manually predetermined set of entity classes for which we expect to have news coverage. The number of analyzed entity classes is 27, including INLINEFORM0 entities with at least one news reference. The entity classes were selected from the DBpedia class ontology. Figure FIGREF42 shows the number of entities per class for the years (2009-2014).
News Articles. We extract all news references from the collected Wikipedia entity pages. The extracted news references are associated with the sections in which they appear. In total there were INLINEFORM0 news references, and after crawling we end up with INLINEFORM1 successfully crawled news articles. The details of the news article distribution, and the number of entities and sections from which they are referred are shown in Table TABREF44 .
Article-Entity Ground-truth. The dataset comprises of the news and entity pairs INLINEFORM0 . News-entity pairs are relevant if the news article is referenced in the entity page. Non-relevant pairs (i.e. negative training examples) consist of news articles that contain an entity but are not referenced in that entity's page. If a news article INLINEFORM1 is referred from INLINEFORM2 at year INLINEFORM3 , the features are computed taking into account the entity profiles at year INLINEFORM4 .
Article-Section Ground-truth. The dataset consists of the triple INLINEFORM0 , where INLINEFORM1 , where we assume that INLINEFORM2 has already been determined as relevant. We therefore have a multi-class classification problem where we need to determine the section of INLINEFORM3 where INLINEFORM4 is cited. Similar to the article-entity ground truth, here too the features compute the similarity between INLINEFORM5 , INLINEFORM6 and INLINEFORM7 .
Data Pre-Processing
We POS-tag the news articles and entity profiles INLINEFORM0 with the Stanford tagger BIBREF21 . For entity linking the news articles, we use TagMe! BIBREF22 with a confidence score of 0.3. On a manual inspection of a random sample of 1000 disambiguated entities, the accuracy is above 0.9. On average, the number of entities per news article is approximately 30. For entity linking the entity profiles, we simply follow the anchor text that refers to Wikipedia entities.
Train and Testing Evaluation Setup
We evaluate the generated supervised models for the two tasks, AEP and ASP, by splitting the train and testing instances. It is important to note that for the pairs INLINEFORM0 and the triple INLINEFORM1 , the news article INLINEFORM2 is referenced at time INLINEFORM3 by entity INLINEFORM4 , while the features take into account the entity profile at time INLINEFORM5 . This avoids any `overlapping' content between the news article and the entity page, which could affect the learning task of the functions INLINEFORM6 and INLINEFORM7 . Table TABREF47 shows the statistics of train and test instances. We learn the functions at year INLINEFORM8 and test on instances for the years greater than INLINEFORM9 . Please note that we do not show the performance for year 2014 as we do not have data for 2015 for evaluation.
Article-Section Placement
Here we show the evaluation setup for ASP task and discuss the results with a focus on three main aspects, (i) the overall performance across the years, (ii) the entity class specific performance, and (iii) the impact on entity profile expansion by suggesting missing sections to entities based on the pre-computed templates.
Baselines. To the best of our knowledge, we are not aware of any comparable approach for this task. Therefore, the baselines we consider are the following:
S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2
S2: Place the news into the most frequent section in INLINEFORM0
Learning Models. We use Random Forests (RF) BIBREF23 and Support Vector Machines (SVM) BIBREF24 . The models are optimized taking into account the features in Table TABREF31 . In contrast to the AEP task, here the scale of the number of instances allows us to learn the SVM models. The SVM model is optimized using the INLINEFORM0 loss function and uses the Gaussian kernels.
Metrics. We compute precision P as the ratio of news for which we pick a section INLINEFORM0 from INLINEFORM1 and INLINEFORM2 conforms to the one in our ground-truth (see Section SECREF40 ). The definition of recall R and F1 score follows from that of precision.
Figure FIGREF66 shows the overall performance and a comparison of our approach (when INLINEFORM0 is optimized using SVM) against the best performing baseline S2. With the increase in the number of training instances for the ASP task the performance is a monotonically non-decreasing function. For the year 2009, we optimize the learning objective of INLINEFORM1 with around 8% of the total instances, and evaluate on the rest. The performance on average is around P=0.66 across all classes. Even though for many classes the performance is already stable (as we will see in the next section), for some classes we improve further. If we take into account the years between 2010 and 2012, we have an increase of INLINEFORM2 P=0.17, with around 70% of instances used for training and the remainder for evaluation. For the remaining years the total improvement is INLINEFORM3 P=0.18 in contrast to the performance at year 2009.
On the other hand, the baseline S1 has an average precision of P=0.12. The performance across the years varies slightly, with the year 2011 having the highest average precision of P=0.13. Always picking the most frequent section as in S2, as shown in Figure FIGREF66 , results in an average precision of P=0.17, with a uniform distribution across the years.
Here we show the performance of INLINEFORM0 decomposed for the different entity classes. Specifically we analyze the 27 classes in Figure FIGREF42 . In Table TABREF68 , we show the results for a range of years (we omit showing all years due to space constraints). For illustration purposes only, we group them into four main classes ( INLINEFORM1 Person, Organization, Location, Event INLINEFORM2 ) and into the specific sub-classes shown in the second column in Table TABREF68 . For instance, the entity classes OfficeHolder and Politician are aggregated into Person–Politics.
It is evident that in the first year the performance is lower in contrast to the later years. This is due to the fact that as we proceed, we can better generalize and accurately determine the correct fit of an article INLINEFORM0 into one of the sections from the pre-computed templates INLINEFORM1 . The results are already stable for the year range INLINEFORM2 . For a few Person sub-classes, e.g. Politics, Entertainment, we achieve an F1 score above 0.9. These additionally represent classes with a sufficient number of training instances for the years INLINEFORM3 . The lowest F1 score is for the Criminal and Television classes. However, this is directly correlated with the insufficient number of instances.
The baseline approaches for the ASP task perform poorly. S1, based on lexical similarity, has a varying performance for different entity classes. The best performance is achieved for the class Person – Politics, with P=0.43. This highlights the importance of our feature choice and that the ASP cannot be considered as a linear function, where the maximum similarity yields the best results. For different entity classes different features and combination of features is necessary. Considering that S2 is the overall best performing baseline, through our approach INLINEFORM0 we have a significant improvement of over INLINEFORM1 P=+0.64.
The models we learn are very robust and obtain high accuracy, fulfilling our pre-condition for accurate news suggestions into the entity sections. We measure the robustness of INLINEFORM0 through the INLINEFORM1 statistic. In this case, we have a model with roughly 10 labels (corresponding to the number of sections in a template INLINEFORM2 ). The score we achieve shows that our model predicts with high confidence with INLINEFORM3 .
The last analysis is the impact we have on expanding entity profiles INLINEFORM0 with new sections. Figure FIGREF70 shows the ratio of sections for which we correctly suggest an article INLINEFORM1 to the right section in the section template INLINEFORM2 . The ratio here corresponds to sections that are not present in the entity profile at year INLINEFORM3 , that is INLINEFORM4 . However, given the generated templates INLINEFORM5 , we can expand the entity profile INLINEFORM6 with a new section at time INLINEFORM7 . In details, in the absence of a section at time INLINEFORM8 , our model trains well on similar sections from the section template INLINEFORM9 , hence we can predict accurately the section and in this case suggest its addition to the entity profile. With time, it is obvious that the expansion rate decreases at later years as the entity profiles become more `complete'.
This is particularly interesting for expanding the entity profiles of long-tail entities as well as updating entities with real-world emerging events that are added constantly. In many cases such missing sections are present at one of the entities of the respective entity class INLINEFORM0 . An obvious case is the example taken in Section SECREF16 , where the `Accidents' is rather common for entities of type Airline. However, it is non-existent for some specific entity instances, i.e Germanwings airline.
Through our ASP approach INLINEFORM0 , we are able to expand both long-tail and trunk entities. We distinguish between the two types of entities by simply measuring their section text length. The real distribution in the ground truth (see Section SECREF40 ) is 27% and 73% are long-tail and trunk entities, respectively. We are able to expand the entity profiles for both cases and all entity classes without a significant difference, with the only exception being the class Creative Work, where we expand significantly more trunk entities.
Conclusion and Future Work
In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating. The process consists of two stages. In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page. In the second stage, article–section placement, we determine the best fitting section in an entity page. Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes. This allows us to add missing sections to entity pages. We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes. For the first stage, we achieve an overall performance with P=0.93, R=0.514 and F1=0.676, outperforming our baseline competitors significantly. For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates. The overall performance across different classes is P=0.844, R=0.885 and F1=0.860.
In the future, we will enhance our work by extracting facts from the suggested news articles. Results suggest that the news content cited in entity pages comes from the first paragraphs. However, challenging task such as the canonicalization and chronological ordering of facts, still remain.

Question: What baseline model is used?
Answer: For Article-Entity placement, they consider two baselines: the first one using only salience-based features, and the second baseline checks if the entity appears in the title of the article. 

For Article-Section Placement, they consider two baselines: the first picks the section with the highest lexical similarity to the article, and the second one picks the most frequent section.
B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 ., B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 .

, S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2, S2: Place the news into the most frequent section in INLINEFORM0