Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text summarization as well as a useful summary of those texts including relevant document sentences. This study select...

The MUSEEC (MUltilingual SEntence Extraction and Compression) summarization tool implements several extractive summarization techniques – at the level of complete and compressed sentences – that can be applied, with some minor adaptations, to documents in multiple languages. The current version of MUSEEC provides the following summarization methods: (1) MUSE – a supervised summarizer, based on ...

Multi-document summarization is the automatic production of a unique summary from a collection of texts. This task has become very important, since it assists the information processing in days where the amount of information is growing considerably. In this paper, we propose a statistical generative approach for multi-document summarization. In particular, we formulate the multi-document summa...

Extractive multi-document summarization is mostly treated as a sentence ranking problem. Existing graph-based ranking methods for key-sentence extraction usually attempt to compute a global importance score for each sentence under a single relation. Motivated by the fact that both documents and sentences can be presented by a mixture of semantic topics detected by Latent Dirichlet Allocation (L...