Synopsi aimed at the design and development of a text summarization prototype based on the text extract summarization paradigm. The developed methodology computes a score for each sentence in the text to be summarized, depending on the sentence’s importance in the text. Sentence score calculation is based on a set of sentence and text features, ranging from termhood of the sentence’s nominal chunks to actual sentence position in the text. Highly-scoring sentences are then aggregated and presented in the same order as in the text. Since, the sentences that are thus extracted present a certain degree of incoherence, methods for improving coherence, cohesion and coverage have been investigated.
The expected benefit of automatic text summarization systems is multi-dimensional. On the one hand information dissemination is largely accelerated by the reduction of the time needed to compile a summary. On the other hand, summaries help :
- Keep people informed of the very recent developments in their areas of interest
- Reduce reading time
- Allow for better selection of those articles that need to be read in full
- Make bibliographic research easier
- Enable a less costly transmission of information
- Help information providers overcome current size constraints, e.g. in wap enabled applications