Massimo Mauro, Sergio Benini, N. Adami, A. Signoroni, R. Leonardi, Luca Canini
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引用次数: 0
Abstract
In this work we present a free Web API for single and multi-text summarization. The summarization algorithm follows an extractive approach, thus selecting the most relevant sentences from a single document or a document set. It integrates in a novel pipeline different text analysis techniques - ranging from keyword and entity extraction, to topic modelling and sentence clustering - and gives SoA competitive results. The application, written in Python, supports as input both plain texts and Web URLs. The API is publicly accessible for free using the specific conference token1 as described in the reference page2. The browser-based demo version, for summarization of single documents only, is publicly accessible at http://yonderlabs.com/demo.
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