Different Types of Automated and Semi-automated Semantic Storytelling: Curation Technologies for Different Sectors
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Many industries face an increasing need for smart systems that support the processing and generation of digital content. This is both due to an ever increasing amount of incoming content that needs to be processed faster and more efficiently, but also due to an ever increasing pressure of publishing new content in cycles that are getting shorter and shorter. In a research and technology transfer project we develop a platform that provides content curation services that can be integrated into Content Management Systems, among others. In the project we develop curation services, which comprise semantic text and document analytics processes as well as knowledge technologies that can be applied to document collections. The key objective is to support digital curators in their daily work, i.e., to (semi-)automate processes that the human experts are normally required to carry out intellectually and, typically, without tool support. The goal is to enable knowledge workers to become more efficient and more effective as well as to produce high-quality content. In this article we focus on the current state of development with regard to semantic storytelling in our four use cases.
KeywordsIncoming Content Technology Transfer Projects Storytelling Capabilities Story Paths Named Entity Linking
Digital content and online media have reached an unprecedented level of relevance and importance, especially with regard to commercial but also political and societal aspects. One of the many technological challenges refers to better support and smarter technologies for digital content curators, i.e., persons, who work primarily at and with a computer, who are facing an ever increasing incoming stream of heterogeneous information and who create, in a general sense, new content based on the requirements, demands, expectations and conventions of the sector they work in. For example, experts in a digital agency build websites or mobile apps for clients who provide documents, data, pictures, videos and other assets that are processed, sorted, augmented, arranged, designed, packaged and then deployed. Knowledge workers in a library digitise a specific archive, add metadata and critical edition information and publish the archive online. Journalists need to stay on top of the news stream including blogs, microblogs, newswires etc. in order to produce a new article on a breaking topic. A multitude of examples exist in multiple sectors and branches of media (television, radio, blogs, journalism etc.). All these different professional environments can benefit immensely from semantic technologies that support knowledge workers, who typically work under high time pressure, in their activities: finding relevant information, highlighting important concepts, sorting incoming documents, translating articles in foreign languages, suggesting interesting topics etc. We call these different semantic services, that can be applied flexibly in different professional environments that all have to do with the processing, analysis, translation, evaluation, contextualisation, verification, synthesis and production of digital information, Curation Technologies.
The activities reported in this paper are carried out in the context of a two-year research and technology transfer project, Digital Curation Technologies1 in which DFKI collaborates with four SME companies that operate in four sectors (3pc: public archives; Kreuzwerker: print journalism; Condat: television and media; ART+COM: museum and exhibition design). We develop, in prototypically implemented use cases, a flexible platform that provides generic curation services such as, e.g., summarisation, named entity recognition, entity linking and machine translation (Bourgonje et al. 2016a, b). These are integrated into the partners’ in-house systems and customised to their domains so that the content curators who use these systems can do their jobs more efficiently, more easily and with higher quality. Their tasks involve processing, analysing, skimming, sorting, summarising, evaluating and making sense of large amounts of digital content, out of which a new piece of digital content is created, e.g., an exhibition catalogue, a news article or an investigative report.
We mainly work with self-contained document collections but our tools can also be applied to news, search results, blog posts etc. The key objective is to shorten the time it takes digital curators to familiarise themselves with a large collection by extracting relevant data and presenting the data in a way that enables the user to be more efficient, especially when they are not domain experts.
We develop modular language and knowledge technology components that can be arranged in workflows. Based on their output, a semantic layer is generated on top of a document collection. It contains various types of metadata as annotations that can be made use of in further processing steps, visualisations or user interfaces. Our approach bundles a flexible set of semantic services for the production of digital content, e.g., to recommend or to highlight interesting and unforeseen storylines or relations between entities to human experts. We call this approach Semantic Storytelling.
In this article we concentrate on the collaboration between the research partner and the four SME companies. For each use case we present a prototype application, all of which are currently in experimental use in these companies.
2 Curation Technologies
The curation services are made available through a shared platform and RESTful APIs (Bourgonje et al. 2016a; Moreno-Schneider et al. 2017a; Bourgonje et al. 2016b; Srivastava et al. 2016). They comprise modules that either work on their own or that can be arranged as workflows.2 The various modules analyse documents and extract information to be used in content curation scenarios. Interoperability between the modules is achieved through the NLP Interchange Format (NIF) (Sasaki et al. 2015). NIF allows for the combination of web services in a decentralised way, without hard-wiring specific pipelines. In the following we briefly present selected curation services.
2.1 Named Entity Recognition and Named Entity Linking
First we convert every document to NIF and then perform Named Entity Recognition (NER). NER consists of two different approaches that allow training with annotated data and/or to use dictionaries. Afterwards the service attempts to look up any named entity in its (language-specific) DBpedia page using DBpedia Spotlight (2016) to extract additional information using SPARQL.
2.2 Geographical Localisation Module and Map Visualisations
The geographical location module uses SPARQL and the Geonames ontology (Wick 2015) to retrieve the latitude and longitude of a location as specified in its DBpedia entry. The module also computes the mean and standard deviation value for latitude and longitude of all identified locations in a document. With this information we can position a document on a map visualisation.
2.3 Temporal Expression Analysis and Timelining
The temporal expression analyser consists of two approaches that can process German and English natural language text, i.e. a regular expression grammar and a modified implementation of HeidelTime (Strötgen and Gertz 2013). After identification, temporal expressions are normalised to a shared format and added to the NIF representation to enable reasoning over temporal expressions and also for archiving purposes. The platform adds document-level statistics based on normalised temporal values. These can be used to position a document on a timeline.
2.4 Text Classification and Document Clustering
We provide a generic classification service, which is based on Mallet (McCallum 2002). It assigns topics or domains such as “politics” or “sports” to documents when labeled training data is available. Annotated topics are stored in the NIF representation as RDF. Unsupervised document clustering is performed using the Gensim toolkit (Řehůřek and Sojka 2010). For the purpose of this paper we performed experiments with a bag-of-words approach and with tf/idf transformations for the Latent Semantic Indexing (LSI) (Halko et al. 2011), Latent Dirichlet Allocation (LDA) (Hoffman et al. 2010) and Hierarchical Dirichlet Process (HDP) (Wang et al. 2011) algorithms.
2.5 Coreference Resolution
For the correct interpretation and representation of events and their arguments and components, the resolution of mentions referring to entities that are not identified by the NER component (because they are realised by a pronoun or alternative formulation) is essential. For these cases we implemented a coreference resolution mechanism based on CoreNLP for English (Raghunathan et al. 2010). For German language documents we replicated this multi-sieve approach (Srivastava et al. 2017). This component increases the coverage of the NER and event detection modules.
2.6 Monolingual and Cross-Lingual Event Detection
We implemented a state-of-the-art event detection system based on Yang and Mitchell (2016) to pinpoint words or phrases in a sentence that refer to events involving participants and locations, affected by other events and spatio-temporal aspects. The module is trained on the ACE 2005 data (Doddington et al. 2004), consisting of 529 documents from a variety of sources. We apply the tool to extract generic events from the various datasets in our curation scenarios. We also implemented a cross-lingual event detection system, i.e., we translate non-English documents to English through Moses SMT (Koehn et al. 2007) and detect events in the translated documents using the system described above.
2.7 Single and Multi-document Summarisation
Automatic summarisation refers to reducing input text (from one or more documents) into a shorter version by keeping its main content intact while still conveying the actual desired meaning (Ou et al. 2008; Mani and Maybury 1999). This task typically involves identifying, extracting and reordering the most important sentences from a document (collection) into a summary. We offer three different approaches: centroid-based summarisation (Radev et al. 2000), lexical page ranking (Erkan and Radev 2004), and cluster-based link analysis (Wan and Yang 2008).
2.8 User Interaction in the Curation Technologies Prototypes
Our primary goal is to support knowledge workers by automating some of their typical processes. This is why all implemented user interfaces are inherently interactive. By providing feedback to, for example, the output of certain semantic services, knowledge workers have some amount of control over the workflow. They are also able to upload existing resources to adapt individual services. For example, we allow users to identify errors in the output (e.g., incorrectly identified entities) and provide feedback to the algorithm; NER allows users to supply dictionaries for entity linking; Event Detection allows users to supply lists of entities for the identification of agents for events.
3 Semantic Storytelling: Four Sector-Specific Use Cases
Generic Semantic Storytelling involves processing a coherent and self-contained collection of documents in order to identify and to suggest, to the human curator, on a rather abstract level, one or more potential story paths, i.e., specific relationships between entities that can then be used for the process of structuring a new piece of content. It was a conscious decision not to artificially restrict the approach (for example, to certain text types) but to keep it broad and extensible so that we can apply it to the specific needs and requirements of different sectors. In one sector a single surprising, hitherto unknown relation between two entities may be enough to construct an actual story while in others we may try to generate the base skeleton of a storyline semi-automatically (Moreno-Schneider et al. 2016). One concrete example are millions of leaked documents, in which an investigative journalist wants to find the most interesting nuggets of information, i.e., surprising relations between different entities, say, politicians and offshore banks. Our services do not necessarily have to exhibit perfect performance because humans are always in the loop in our application scenario. We want to provide robust technologies with broad coverage. For some services this goal can be fulfilled while for others, it is a bit more ambitious.
3.1 Sector: Museums and Exhibitions
The company ART+COM AG is specialised in the design of museums, exhibitions and showrooms. Their creative staff needs to be able to familiarise themselves with new topics quickly to participate in pitches or during the execution of projects. We implemented a graphical user interface (GUI) that supports the knowledge workers’ storytelling capabilities, e.g., for arranging exhibits in a room or for arranging the rooms themselves, by supporting and improving the task of curating incoming content. The GUI enables the effective interaction with the content and the semantic analysis layer. Users can get a quick overview of a specific topic or drill down into the semantic knowledge base to explore deeper relationships.
Initial user research provided valuable insights into the needs of the knowledge workers in this specific use case, especially regarding the kinds of tools and environments each user is familiar with as well as extrapolating their usage patterns (Rehm et al. 2017a). Incoming content materials, provided by clients, include large heterogeneous document collections, e.g., books, images, scientific papers etc. We subdivide the curation process into the phases search, evaluate, organise.
3.2 Sector: Public Archives, Libraries, Digital Humanities
With this tool the content curator can interactively put together a story based on the semantically enriched content. In the example use case we work with a set of approx. 2,800 letters exchanged between the German architect Erich Mendelsohn (1887–1953) and his wife Luise, both of whom travelled frequently. The collection contains 2,796 letters, written between 1910 and 1953, with a total of 1,002,742 words (359 words per letter on average) on more than 11,000 sheets of paper. Most are in German (2,481), the rest is written in English (312) and French (3). The letters were scanned, transcribed and critically edited; photos and metadata are available; this research was carried out in a project that the authors of the present paper are not affiliated with (Bienert and de Wit 2014). In the letters the Mendelsohns discuss their private and professional lives, their relationship, meetings with friends and business partners, and also their travels.
Automatically extracted movement action events (MAEs)
Another train stopped [...] this would be the train with which Eric had to leave Cleveland
Eric, Cleveland, , , , train
Because I have to leave on the 13th for Chicago
I (Erich), Croton on Hudson, NY, Chicago, 13th Dec. 1945, , 
April 5th 48 Sweetheart - Here I am - just arrived in Palm Springs [...]
I (Erich), , Palm Springs, , 5th April 1948, 
Thompsons are leaving for a week - [...] at the Beverly Hills on Thursday night!!
Thompsons, , Beverly Hills, 8th July, , 
3.3 Sector: Journalism
Journalists write news articles based on information collected from different sources (news agencies, media streams, other news articles, sources, etc.). Research is needed on the topic and domain at hand to produce a high-quality piece. Facts have to be checked, different view points considered, information from multiple sources combined in a sensible way. The resulting piece usually combines new, relevant and surprising information regarding the event reported upon. While the amount of available information is increasing on a daily basis, the journalist’s ability to go through all the data is decreasing, which is why smart technology support is needed. We want to enable journalists interactively to put together a story based on semantic content enrichment. In our various use cases, different parts of the content function as atomic building blocks (sentences, paragraphs, documents). For this use case we focus, for now, upon document-level building blocks for generating stories, i.e., documents can be rearranged, included and deleted from a storyline.3
The company Condat AG develops software that is used in television stations, supporting journalists to put together, e.g., news programmes, by providing access to databases of metadata-rich archives that consist of media fragments. We developed recommendation, metadata extraction and multi-document summarisation services that enable editors to process large amounts of data, to find updates of stories about events already seen and to identify relevant, but rarely used, media, to provide a certain level of surprise in the storytelling.
We group incoming news through document clustering. First we perform topic modeling using a bag-of-words representation with the vectors based on tf/idf values (Sect. 2.4). The clusters are fed to the multi-document summarisation service, summarising on a per-topic-basis and then to the timelining and multi-document summarisation service (fixed summary length of 200 words). Given that the same number of documents was clustered into topics using six different models (bag of words and tf/idf for HDP, LDA, and LSI each) and that the length of the summary for each topic was fixed at a maximum of 200 words, we discovered that the bag of words approach yields lengthier summaries than tf/idf. Additionally, in 90% of the cases, cluster-based link analysis outperformed all other approaches in terms of summary length.
4 Related Work
Semantic Storytelling can be defined as the generation of stories, identification of story paths or recommendation of storylines based on a certain set of content using a concrete narrative style or voice. Thus, automatic storytelling consists of two components: a semantic representation of story structure, and the ability to automatically visualise or generate a story from this semantic representation using some form of Natural Language Generation (NLG) (Rishes et al. 2013). In NLG, notable related work is described, among others, by (Jorge et al. 2013; Dionisio et al. 2016; Mazeika 2016; Farrell and Ware 2016). While an interesting discipline that is essential to applying any system aimed at automatically generating stories, especially regarding surface realisation, we primarily focus on the generation of the semantic structure of the story.
Bowden et al. (2016) describe what a story is and how to convert it into a dialogue story, i.e., a system capable of telling a story and then retelling it in different settings to different audiences. They define a story as a set of events, characters, and properties of the story, as well as relations among them, including reactions of characters to story events. For this they use EST (Rishes et al. 2013), a framework that produces a story annotated for the tool Scheherazade as a list of Deep Syntactic Structures, a dependency-tree structure where each node contains the lexical information for the important words in a sentence. Kybartas and Bidarra (2015) present GluNet, a flexible, open source knowledge-base that integrates a variety of lexical databases and facilitates commonsense reasoning for the definition of stories.
Similar to our approach is the work of Samuel et al. (2016). They describe a writing assistant that provides suggestions for the actions of characters. This assistant is meant to be a “playful tool”, which is intended to “serve the role of a digital writing partner”. We perform similar processes when extracting events and entities from a document collection but our system operates on a more general level and is meant to be applied in different professional sectors.
Several related approaches concentrate on specific domains. A few systems focus on providing content for entertainment purposes (Wood 2008), others focus on storytelling in gaming (Gervás 2013), for recipes (Cimiano et al. 2013; Dale 1989) or weather reports (Belz 2008), requiring knowledge about characters, actions, locations, events, or objects that exist in this particular domain (Riedl and Young 2010; Turner 2014). A closely related approach is the one developed by Poulakos et al. (2015), which presents “an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis”.
We developed curation technologies that can be applied in the sector-specific use cases of companies active in different sectors and content curation use cases. The partner companies are in need of semantic storytelling solutions that support their own in-house or their customers’ content curators putting together new content products, either museum exhibitions, interactive online versions of public archives, news articles or news programmes. The motivation is to make the curators more efficient, to delegate routine tasks to the machine and to enable curators to produce higher quality products because the machine may be able to identify interesting, novel, eye-opening relationships between two pieces of content that a human is unable to recognise. The technologies, prototypically implemented and successfully applied in four sectors, show very promising results (Rehm et al. 2017a), even though the individual implementations of the interactive storytelling approaches are quite specific.
For the museums and exhibitions case we developed a prototype that allows the interactive curation, analysis and exploration of the background material of a new exhibition, supporting the knowledge workers who design the exhibition in their storytelling capabilities by helping them to identify interesting relationships. For the public archive case we implemented a prototype that semantically enriches a collection of letters so that a human expert can more efficiently tell interesting stories about the content – in our example we help the human curator to produce a travelogue about the different trips of the Mendelsohns as an alternative “view” upon the almost 2,800 letters. For newspaper journalism, we annotate named entities to generate clusters of documents that can be used as storylines. For the television case we applied a similar approach but we cluster events instead of named entities (including timelining).
This article provides a current snapshot of the technologies and approaches developed in our project. In a planned follow-up project we will experiment with Natural Language Generation approaches in order to produce natural language text – either complete documents or draft skeletons to be checked, revised and completed by human experts – based on automatically extracted information and on external knowledge provided as Linked Open Data. For this approach we anticipate a whole new set of challenges with regard to semantic storytelling.
The authors would like to thank the reviewers for their insightful comments and suggestions. The project “Digitale Kuratierungstechnologien” (DKT) is supported by the German Federal Ministry of Education and Research (BMBF), “Unternehmen Region”, instrument Wachstumskern-Potenzial (no. 03WKP45). More information: http://www.digitale-kuratierung.de.
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