Sentence compression techniques based on linguistically-motivated syntactic rules have proved effective in single-document summarization tasks. The addition of topic terms yields state-of-the-art performance, according to previous evaluations. Since “trimming” rules must be applied successively, optimal rule ordering presents a challenge. This paper describes the Multi-Candidate Reduction (MCR)...

The combination of web document contents, sentences and users’ comments from social networks provides a viewpoint of a web document towards a special event. This paper proposes a framework named SoRTESum to take advantage of information from Twitter viz. Diversity and reflection of document content to generate high-quality summaries by a novel sentence similarity measurement. The framework firs...

Extractive summarizing can be divided into several steps. Preprocessing is a first of them and it usually includes: sentence splitting, stop words removal, stemming etc. In processing step, sentence features are calculated and then weights are assigned to these features using machine learning or heuristic methods. Those features are numerical characteristics of each sentence (e.g. sentence loca...

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of...

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of...

Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent developments in neural-attention based sequence-to-sequence models have led to state-of-the-art results on the task of abstractive generic sin...