Deliverable 6.1 Infrastructure for Extractive Summarization

SKATER Internal Report: software of infrastructure for extractive Summarization (work carried out until December 2013)Preprin

MEAD - A Platform for Multidocument Multilingual Text Summarization

This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection

Summarizing a multimodal set of documents in a Smart Room

This article reports an intrinsic automatic summarization evaluation in the scientific lecture domain. The lecture takes place in a Smart Room that has access to different types of documents produced from different media. An evaluation framework is presented to analyze the performance of systems producing summaries answering a user need. Several ROUGE metrics are used and a manual content responsiveness evaluation was carried out in order to analyze the performance of the evaluated approaches. Various multilingual summarization approaches are analyzed showing that the use of different types of documents outperforms the use of transcripts. In fact, not using any part of the spontaneous speech transcription in the summary improves the performance of automatic summaries. Moreover, the use of semantic information represented in the different textual documents coming from different media helps to improve summary quality.Peer ReviewedPostprint (author’s final draft

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.This research has been supported by the project “Desarrollo de Técnicas Inteligentes e Interactivas de Minería de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01)

An Unsupervised Approach to Biography Production using Wikipedia

We describe an unsupervised approach to multi-document sentence-extraction based summarization for the task of producing biographies. We utilize Wikipedia to automatically construct a corpus of biographical sentences and TDT4 to construct a corpus of non-biographical sentences. We build a biographical-sentence classifier from these corpora and an SVM regression model for sentence ordering from the Wikipedia corpus. We evaluate our work on the DUC2004 evaluation data and with human judges. Overall, our system significantly outperforms all systems that participated in DUC2004, according to the ROUGE-L metric, and is preferred by human subjects

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features