Supporting Journalism by Combining Neural Language Generation and Knowledge Graphs
Authors: Marco Cremaschi, Federico Bianchi, Andrea Maurino, Andrea Primo Pierotti (Università di Milano Bicocca, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Natural Language Generation is a field that is becoming relevant in several domains, including journalism. Natural Language Generation techniques can be of great help to journalists, allowing a substantial reduction in the time required to complete repetitive tasks. In this position paper, we enforce the idea that automated tools can reduce the effort required to journalists when writing articles; at the same time we introduce GazelLex (Gazette Lexicalization), a prototype that covers several steps of Natural Language Generation, in order to create soccer articles automatically, using data from Knowledge Graphs, leaving journalists the possibility of refining and editing articles with additional information. We shall present our first results and current limits of the approach, and we shall also describe some lessons learned that might be useful to readers that want to explore this field.

Introduction
Although automation is a phenomenon that is becoming more and more visible today, there are specialised jobs that require human effort to be completed. The job of a journalist is among these (Örnebring, 2010). However, recent technological progress in the field of Natural Language Generation (NLG) and the use of increasingly sophisticated techniques of artificial intelligence allow the use of software capable of writing newspaper articles almost indistinguishable from human ones. These techniques can help journalists reduce the effort needed for repetitive tasks, such as data collection and drafting writing. The name given to this phenomenon is Automated Journalism; this new type of journalism uses algorithms to generate news under human supervision. During the past years, several newsrooms have begun to experiment this technology: Associated Press, Forbes, Los Angeles Times, and ProPublica are among the first, but adoption could spread out soon (Graefe, 2016). Automated Journalism can bring a massive change to the sector: writing news is a business that endeavours to minimise costs while maintaining maximum efficiency and full speed, and thanks to this software the above-mentioned objectives can be achieved, generating good-quality articles (van Dalen, 2012). This new technology provides many advantages: the most evident are speed and the scale of news coverage. Of course, there are also problems and limitations. One of the most relevant is the dependence from structured data (Graefe, 2016), that is the reason why sports reports, financial articles, and forecasts are the most covered topics by software: they are all domains where the complexity of the topic can be managed from software using structured data. Similar structured data are not always available in other fields. In order to generate valuable text, approaches considering data contained in the Knowledge Graphs (KGs) have recently been introduced in literature (Gardent et al., 2017; Trisedya et al., 2018). A Knowledge Graph (KG) describes real-world entities and the relations between them. KGs are an essential source of information, and their features allow the use of this information in different contexts, such as link prediction (Trouillon et al., 2016) and recommendation (Zhang et al., 2016). Popular KGs are the Google Knowledge Graph, Wikidata and DBpedia (Auer et al., 2007). Entities are defined in an ontology and thus can be classified using a series of types. The primary element of a KG to store entities information is a Resource Description Framework (RDF) triple in the format hsubject; predicate; objecti. As RDF triples open many possibilities in Web data representation, utilising this data also in the NLG context is valuable (Perera et al., 2016). Interlinked KGs can be used to automatically extend the information relating to a given entity in an article. In our solution, we use DBpedia, one of the fastest growing Linked Data resources that is available free of charge; it is characterised by a high number of links from the Linked Data Cloud. DBpedia is thus a central interlinking hub, an access point for retrieving information to be inserted in an article, as specified below. Up to 2010, commercial providers in the NLG field were not popular, but in the last years few companies have started to provide this kind of services. In 2016 there were 13 companies covering this field (Drr, 2016) (e.g., AutomatedInsights, NarrativeScience). Approaches that try to integrate deep networks and text generation are now common in literature (Gardent et al., 2017). These automated tools are going to become a standard method to help journalists during the news writing process. We shall concentrate on examples of related work in the context of lexicalization from RDF data, we shall refer to surveys from the state of the art for a more detailed overview of the field (Reiter and Dale, 1997; Gatt and Krahmer, 2018; Moussallem et al., 2018). Semantic web technologies like RDF can be used to enhance the power of current algorithms (Bouayad-Agha et al., 2012). The WebNLG challenge (Gardent et al., 2017) has been introduced to study the possibilities given by the combination of deep learning techniques and semantic web technologies. In a similar context, an approach based on Long Short-Term Memory (LSTM) networks has been proposed to generate text lexicalizations from RDF triples (Trisedya et al., 2018). In this work, we aim to describe what is the possible automation process that can be used to help journalists in the news writing process. At the same time we describe a new prototype we have created to support journalistic activities, GazelLex (Gazette Lexicalization). GazelLex, through the use of deep learning techniques implements a Neural Machine Translation (NMT) approach to generate articles (sentences) starting from data composed by RDF triples. GazelLex is also able to generate videos containing the images and the prominent information of the article, and to generate audio using a speech synthesis module (Figure 1). To the best of our knowledge, our prototype is the first to provide an all-in-one integrated approach to NLG with RDF triples in the context of helping journalists in writing articles. This paper is structured as follows: in Section 2, we analyse the state-of-the-art on Natural Language Generation, showing that these methods to generate natural language are becoming popular. In Section 3 we describe our prototype, GazelLex, that combines neural methods and knowledge graphs to create soccer articles and describe how this kind of tools can be of help to journalism. In Section 4 we show a preliminary experimental analysis, while in Section 5 we provide conclusions.

Conclusion
In this position paper we have analysed the future possibilities given by automated journalism. We have summarised the current state of art on this topic showing that there is an increasing interest towards automated natural language generation for the news sector. While hereby, we showed an application related to the soccer domain, the principles and the methodologies described are general, and they can be used in other fields (e.g., finance, weather reporting). We strongly believe that these tools can greatly help journalists in working on what is really important (e.g., investigation, fact checking), leaving high effort, but low value tasks to computers. The prototype we have described is a first step towards this automated process and its results are surely promising.