The main goal is to create a system that can automatically summarize news articles without the key phrases and sentences being modified. CADChain's approach is based on linguistic knowledge and uses existing AI models that can be trained further.
The first step is to use our linguistic knowledge to define what aspects in a sentence can be removed when a short summary is requested and what would be considered 'additional detail'. This is called our Nearest Neighbor expansion. The next step is to train AI models to value each derived sentence for importance and to use the importance classification to include more sentences in the summary. The third step is to wrap this in a user-friendly interface which makes the process understandable for the writers and at the same time allow custom entries or changes to the result of the algorithm. The journalist can adjust more specific parameters for their article's summarization and directly see the result. Summarization length can be variably defined based on parameters such as article length, requested duration, method of increasing the size of the summary.
This project has indirectly received funding from the European Union’s Horizon 2020 research and innovation programme under REACH Incubator
(Grant Agreement no. 951981).
