Modeling Event Importance for Ranking Daily News Events

Setty, Vinay; Anand, Abhijit; Mishra, Abhisek; Anand, Avishek

doi:10.1145/3018661.3018728

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…For example, to rank news articles related to a query entity, Singh et al [54] employ a diversified ranking model based both on the aspect and temporal dimension. Approaches such as the one proposed by Setty et al [55] impose methods to rank the importance of events, but without taking into account the specific timeline entity. In comparison to these approaches, the task addressed in our work is more specific, as it considers the relevance of individual temporal relations to a timeline entity.…”

Section: Biographical Timeline Generationmentioning

confidence: 99%

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Gottschalk

Demidova

2019

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One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation -EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.

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Section: Biographical Timeline Generationmentioning

confidence: 99%

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Gottschalk

Demidova

2019

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“…In open news corpus, event and their types are both unknown, so if the accuracy in event detection improves, the accuracy in event-type detection increases, and vice versa. The studies that focus only on event detection discover events by clustering news articles based on the similar word [18,67] or entity distributions [38,54]. They all rely on ad-hoc methods to guess the number of events and cannot detect the prerequisite to schema induction-event types.…”

Section: Figure 1: Framework Overviewmentioning

confidence: 99%

Open-Schema Event Profiling for Massive News Corpora

Yuan

Ren

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem-schema-based news event profiling-profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots' expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling.

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“…A common approach to measure notability of a news event is to track it through a proxy metric. For example, Naseri et al [7] decide whether an article describes a notable event by counting the user interactions, while Setty et al [10] cluster together similar news articles and then use the cluster size to decide if the common theme is notable. Wang et al [12] propose a recommendation framework that takes as input a stream of news and predicts the user's click-through rate.…”

Section: Introductionmentioning

confidence: 99%

Identifying Notable News Stories

Saravanou

Stefanoni²,

Meij³

2020

Lecture Notes in Computer Science

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The volume of news content has increased significantly in recent years and systems to process and deliver this information in an automated fashion at scale are becoming increasingly prevalent. One critical component that is required in such systems is a method to automatically determine how notable a certain news story is, in order to prioritize these stories during delivery. One way to do so is to compare each story in a stream of news stories to a notable event. In other words, the problem of detecting notable news can be defined as a ranking task; given a trusted source of notable events and a stream of candidate news stories, we aim to answer the question: "Which of the candidate news stories is most similar to the notable one?". We employ different combinations of features and learning to rank (LTR) models and gather relevance labels using crowdsourcing. In our approach, we use structured representations of candidate news stories (triples) and we link them to corresponding entities. Our evaluation shows that the features in our proposed method outperform standard ranking methods, and that the trained model generalizes well to unseen news stories.

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Modeling Event Importance for Ranking Daily News Events

Cited by 15 publications

References 32 publications

EventKG – the hub of event knowledge on the web – and biographical timeline generation

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Open-Schema Event Profiling for Massive News Corpora

Identifying Notable News Stories

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