MODELLING NEWS PROGRESSION USING ENTITY-TIMELINES

With the massive amount of news available each day, it can be hard to gain a complete overview over what events are happening and who plays a part in the events. This information overload has two aspects: Firstly it can be hard to figure out what events are important on a day to day basis due to the sheer volume of information presented. Solving this would require some measure of relevance to the reader for each piece of news, eg. if an article describing a drop in stock prices and an article describing politicians voting on whether or not they should allow fracking in an area are both in todays paper, one would have to be able to figure out which of those news would be most relevant to the user and show them only the relevant article. Secondly, it can be hard to discern what the articles are portraying if news are just archived chronologically by publication date. This issue is more about how the information is presented, eg. if a law has been passed which allows for fracking in an area, it raises questions such as how long it has been discussed, who was involved in the voting, who voted what, whether any lobbyists were involved, what arguments were for and against and so on. A system solving this issue will have to grant the user a good overview of the events described in the news. We focus on the second problem of properly conveying information about who and what are involved in events throughout time based on archived news. Providing an overview of archived news is a well studied area, although many focus on presenting topics [1–3]. Topic is a term covering several smaller events e.g. a topic such as "Aalborg University collaborates with city council" could have the smaller events "The city council set aside money for building projects on university campus" and "Architecture students to draw proposals for new sports center on campus". These approaches often group the events into sequences that represents a coherent story related to that issue. They do not however present a story of specific agents involved in the events. We are interested in conveying the story of these agents appearing in the news, e.g. we want to tell the story of how Barack Obama has appeared throughout the news during a span of time, rather than what happened during presidential inaugurations. To do this, we create timelines from the news articles based on the named entities, which are found as in [4], and can roughly be thought of as the proper nouns found in the text. To represent information in a news archive, we use the concept of metro maps presented in [2], which represents several different timelines over the news articles and their intersections with each other. As our main purpose is to convey how named entities are involved in stories, we use metro lines to represent each named entity and metro stops to represent a cluster of news stories forming an event that the named entities take part in. If a named entity takes part in an event, its metro line intersects the metro stop representing that event. An example of this can be seen in figure 1. In the example of a metro line in figure 1, we have 3 metro lines, each representing a named entity: The green line (1) representing the rector of Aalborg University, Per Michael Johansen , the blue line (2) representing the Department of Computer Science on Aalborg University and the red line (3) representing Aalborg University. Those 3 agents are part of a set of news stories covering 4 events, represented in figure 1 by the text in the intersection of the metro lines (4). Those events are made by finding representative sentences in the articles mentioning them based on the clustering done to find them. Lastly, in figure 1 the events are associated with a time (5) representing roughly when it takes place. We concern ourselves mostly with small datasets from one or few sources. The dataset we use for testing is a set of newspaper articles provided by the Danish news agency NORDJYSKE. Although our approach should also be able to handle a multitude of sources each describing the same named entities and events, which many news archives may have, we focus on being able to accurately represent the story of less represented agents such as a local politician, who may be mentioned a few times a month from a local news source rather than representing an agent such as the U.S. president, who is often mentioned in several stories and events on a daily basis. In the following we present an overview of the sections in the paper. Section 2 contains preliminaries, under which we describe terminology and related work. Section 3 describes the concept of named entities, what they are and how we obtain them. Section 4 details how a metro map is constructed. Section 5 gives an overview of how the user studies were set up and the tests conducted. Section 6 shows the results of the user studies and discusses how well they went. Section 7 provides a conclusion.