In this paper we introduce TweetNorm_es, an annotated corpus of tweets in Spanish language, which we make publicly available under the terms of the CC-BY license. This corpus is intended for development and testing of microtext normalization systems. It was created for Tweet-Norm, a tweet normalization workshop and shared task, and is the result of a joint annotation effort from different research groups. In this paper we describe the methodology defined to build the corpus as well as the guidelines followed in the annotation process. We also present a brief overview of the Tweet-Norm shared task, as the first evaluation environment where the corpus was used.

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.

Question Answering (QA) technology aims at providing relevant answers to natural language questions. Most Question Answering research has focused on mining document collections containing written texts to answer written questions. In addition to written sources, a large (and growing) amount of potentially interesting information appears in spoken documents, such as broadcast news, speeches, seminars, meetings or telephone conversations. The QAST track (Question-Answering on Speech Transcripts) was introduced in CLEF to investigate the problem of question answering in such audio documents. This paper describes in detail the evaluation protocol and tools designed and developed for the CLEF-QAST evaluation campaigns that have taken place between 2007 and 2009. We first remind the data, question sets, and submission procedures that were produced or set up during these three campaigns. As for the evaluation procedure, the interface that was developed to ease the assessors’ work is described. In addition, this paper introduces a methodology for a semi-automatic evaluation of QAST systems based on time slot comparisons. Finally, the QAST Evaluation Package 2007-2009 resulting from these evaluation campaigns is also introduced.

This paper reports on the QAST track of CLEF aiming to evaluate Question Answering on Speech Transcriptions. Accessing information in spoken documents provides additional challenges to those of text-based QA, needing to address the characteristics of spoken language, as well as errors in the case of automatic transcriptions of spontaneous speech. The framework and results of the pilot QAst evaluation held as part of CLEF 2007 is described, illustrating some of the additional challenges posed by QA in spoken documents relative to written ones. The current plans for future multiple-language and multiple-task QAst evaluations are described.