Elena Lloret


2019

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Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of L2 Texts?
Tatiana Vodolazova | Elena Lloret
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20%, 40%, 60% and 80%. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with user’s needs and language proficiency.

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The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries
Tatiana Vodolazova | Elena Lloret
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text into its semantic representation. We analyzed the impact of each component of our approach on the quality of generated summaries and tested it on DUC 2002 dataset. Our experiments showed that our approach outperformed other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.

2017

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Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
George Giannakopoulos | Elena Lloret | John M. Conroy | Josef Steinberger | Marina Litvak | Peter Rankel | Benoit Favre
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

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MultiLing 2017 Overview
George Giannakopoulos | John Conroy | Jeff Kubina | Peter A. Rankel | Elena Lloret | Josef Steinberger | Marina Litvak | Benoit Favre
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was widened, bringing together researchers that work on summarization across sources, languages and genres. We summarize the main tasks planned and implemented this year, the contributions received, and we also provide insights on next steps.

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Ultra-Concise Multi-genre Summarisation of Web2.0: towards Intelligent Content Generation
Elena Lloret | Ester Boldrini | Patricio Martínez-Barco | Manuel Palomar
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a)positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.

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Improving the Naturalness and Expressivity of Language Generation for Spanish
Cristina Barros | Dimitra Gkatzia | Elena Lloret
Proceedings of the 10th International Conference on Natural Language Generation

We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language. This inflection module inflects the verbs using an ensemble of trainable algorithms whereas the other types of words (e.g. nouns, determiners, etc) are inflected using hand-crafted rules. We show that our approach achieves 2% higher accuracy than two state-of-art inflection generation approaches. Furthermore, our proposed approach also predicts an extra feature: the inflection of the imperative mood, which was not taken into account by previous work. We also present a user evaluation, where we demonstrate that the proposed method significantly improves the perceived naturalness of the generated language.

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Inflection Generation for Spanish Verbs using Supervised Learning
Cristina Barros | Dimitra Gkatzia | Elena Lloret
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We present a novel supervised approach to inflection generation for verbs in Spanish. Our system takes as input the verb’s lemma form and the desired features such as person, number, tense, and is able to predict the appropriate grammatical conjugation. Even though our approach learns from fewer examples comparing to previous work, it is able to deal with all the Spanish moods (indicative, subjunctive and imperative) in contrast to previous work which only focuses on indicative and subjunctive moods. We show that in an intrinsic evaluation, our system achieves 99% accuracy, outperforming (although not significantly) two competitive state-of-art systems. The successful results obtained clearly indicate that our approach could be integrated into wider approaches related to text generation in Spanish.

2016

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Generating sets of related sentences from input seed features
Cristina Barros | Elena Lloret
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

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Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea
Elena Lloret | Claire Gardent
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

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Analysing the Integration of Semantic Web Features for Document Planning across Genres
Marta Vicente | Elena Lloret
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

2015

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The University of Alicante at MultiLing 2015: approach, results and further insights
Marta Vicente | Óscar Alcón | Elena Lloret
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Input Seed Features for Guiding the Generation Process: A Statistical Approach for Spanish
Cristina Barros | Elena Lloret
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)

2011

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Multi-Document Summarization by Capturing the Information Users are Interested in
Elena Lloret | Laura Plaza | Ahmet Aker
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Finding the Best Approach for Multi-lingual Text Summarisation: A Comparative Analysis
Elena Lloret | Manuel Palomar
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Towards a Unified Approach for Opinion Question Answering and Summarization
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

2010

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Quantifying the Limits and Success of Extractive Summarization Systems Across Domains
Hakan Ceylan | Rada Mihalcea | Umut Özertem | Elena Lloret | Manuel Palomar
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Experiments on Summary-based Opinion Classification
Elena Lloret | Horacio Saggion | Manuel Palomar
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text

2009

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Summarizing Threads in Blogs Using Opinion Polarity
Alexandra Balahur | Elena Lloret | Ester Boldrini | Andrés Montoyo | Manuel Palomar | Patricio Martínez-Barco
Proceedings of the Workshop on Events in Emerging Text Types

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Towards Building a Competitive Opinion Summarization System: Challenges and Keys
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium