1. Open Source SUMMA Platform
Guntis Barzdins (LETA) User Group Meeting 3 20 November 2018
The SUMMA project is funded by the EU H2020 ICT Programme under Grant Agreement

2. Code and Installation https://github.com/summa-platform/summa-oss
SUMMA User Interface
Scales to 400 live TV channels: AWS m5.24xlarge instance per 25 live TV channels
Code and Installation

3. SUMMA Platform TRL 5-8 TRL 3-7 MediaItem Ingestion
250 TV/radio chanels
Text & Social media
Speech Recognition (ASR) 9 languages
Machine Translation to English
Segmentation and Punctuation
Natural Language Understanding (NLU)
Clustering in Storylines
Summarisation
Storylines
MediaItems
Topic Detection
Named Entity Recognition (NER) and Linking (NEL)
Persons
Organizations
GPE
Events a
Knowledge Base (KB) population (Facts about Named Entities)
User eXperience (UX) Interface
Trending view 24h
Named Entities view (KB)
Dynamic Storylines
FreeText search
Scalable to 400 live channels
original
original EN DE AR SP PT RU IR UA LV EN EN
text
annotations + text

4. SUMMA Platform Advantages
Completely self-contained
No dependence on external (cloud) services
All components developed within SUMMA
Scalable for BigData
All NLP modules are Docker containers
Scalable to 400 live streams on 800 servers (e.g. AWS)
No external licencing

5. Multilingual technologies
Open Source SUMMA Platform supports EN, DE, LV

6. Integration Architecture & Scalability for Big Data
All components are Docker containers
Scalability is achieved by launching as many Docker container instances per task as required
Scales to 400 live TV channels

7. Final Scalability Test Sources and Resources

8. Final Scalability Test Conclusions
Shallow stream processing for live video streams (ASR, punctuation, MT, topic detection) is useful for video content monitoring
Natural Language Understanding components (storyline clustering, summarization, NER, NEL, relation extraction, geo-location) are useful only for written text input, but are mostly useless for live video input
Language understanding needs to be grounded in video. LETA submited an ERC grant application: «High Dimensional Representation and Computing: Pixels, Objects, Language»

9. DEMO