Prototypes and technologies

The SUMMA Platform is a tool for aggregating and analysing various news items (text, audio, video). The Platform consists of multiple NLP (natural language processing) modules, including Automatic Speech Recognition (ASR), Machine Translation (MT), Named Entity Linking (NEL), Knowledge Data Base (KDB) , event clustering, topic detection, sentiment detection and story-line summarisation. Each module is developed independently by a team that is focused on that module. The goal of the Baseline Architecture is to provide a maximum independence so that each team is free to choose whatever technologies are most appropriate, on the condition that each module honours the API contract.

It also provides the User eXperience (UX) interface with the relevant visualisations for news data enriched by the above mentioned Natural Language Processing (NLP) modules.

The current SUMMA Integrated Platform UI is presented below, showing Deutsche Welle content processed through the platform. It includes topical highlights, video image and play function, a teaser with a very short description, and a transcript obtained through ASR, in the original language, as well as the English translation, resulting from machine translation.

A new SUMMA UI is expected to be available in the summer of 2017. Clickable wireframes have been jointly developed by user partners DW and BBC and provided to the platform integrator.

The Platform is a common system for all three use cases, thus ensuring a maximum of commonality, shared resources and consistency. Dashboards will be developed on top of that integrated platform, allowing for diversification and customisation in terms of visualisation and user preferences. Also the data journalism use case makes use of the output of the integrated platform.  The dashboards will be developed in the second half of the project.

SUMMA Architecture

The SUMMA Platform architecture has three core goals:

  1. Integrate NLP tools into the common pipeline for both batch and stream processing modes
  2. Provide UX interfaces based on user requirements and use cases
  3. Ensure BigData scalability (ability to process 200–400 live streams)

The SUMMA process flow is captured in the diagram below, indicating the content flow from user input to user output, with SUMMA processing modules in between.

Technology Components

SUMMA encompasses 10 technology components

  • ASR: Speech recognition
  • Meta: Metadata extraction from broadcast media
  • MT: Machine translation
  • CT: Streaming implementation of Storyline Clustering and Topic detection
  • ETL: Entity Tagging \& Linking
  • KB: Knowledge Base Construction
  • FC: Forecasting and Fact Checking
  • SP: Story-level semantic parsing
  • SH: Story highlight generation/summarisation