Two uses of anaphora resolution in summarization

Shinar, J.; Poesio, Massimo; Kabadjov, Mijail; Jeek, Karel

doi:10.1016/j.ipm.2007.01.010

Cited by 121 publications

(68 citation statements)

References 14 publications

(29 reference statements)

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“…under the heading of syntax-based statistical mt as in work by Knight and colleagues [6,13]), and researchers in extractive summarisation have started to develop post-processing text regeneration techniques to improve the coherence and clarity of summaries [27,34,25].…”

Section: Development Of the Grec Tasksmentioning

confidence: 99%

Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

Belz

Kow

Viethen

et al. 2010

Empirical Methods in Natural Language Generation

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Abstract. Until recently, referring expression generation (reg) research focused on the task of selecting the semantic content of definite mentions of listener-familiar discourse entities. In the grec research programme we have been interested in a version of the reg problem definition that is (i) grounded within discourse context, (ii) embedded within an application context, and (iii) informed by naturally occurring data. This paper provides an overview of our aims and motivations in this research programme, the data resources we have built, and the first three sharedtask challenges, grec-msr'08, grec-msr'09 and grec-neg'09, we have run based on the data.

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Section: Development Of the Grec Tasksmentioning

confidence: 99%

Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

Belz

Kow

Viethen

et al. 2010

Empirical Methods in Natural Language Generation

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“…It was decided to selects this feature combination as the baseline. 0.40906 is slightly lower than the DUC baseline of 0.41132 [6] . Despite this fact none of the system settings without the stopwords filtering could outperform it.…”

Section: Resultsmentioning

confidence: 65%

“…The previous work on including anaphora resolution (AR) reported some improvement over the lexical LSA system [6] . However, not all the systems equally benefit from including AR method.…”

Section: Anaphora Resolutionmentioning

confidence: 99%

“…Different approaches select different features and methods, starting from the very basic ones like term frequency [1] , position of the sentence within the original document [2,3] , assigning higher weights to the sentences containing terms of the title [2] and inverse sentence frequency [4] ; or more complex ones including word sense disambiguation [5] , latent semantic analysis and anaphora resolution [6] , textual entailment [7] . However, each of the above mentioned system only focuses on a few distinct features, usually two or three.…”

Section: Introductionmentioning

confidence: 99%

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The role of statistical and semantic features in single-document extractive summarization

Vodolazova

Lloret²,

Muñoz³

et al. 2013

AIR

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This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization. The features experimented with include word frequency, inverse sentence and term frequencies, stopwords filtering, word senses, resolved anaphora and textual entailment. The obtained results demonstrate the relative importance of each feature and the limitations of the tools available. It has been shown that the inverse sentence frequency combined with the term frequency yields almost the same results as the latter combined with stopwords filtering that in its turn proved to be a highly competitive baseline. To improve the suboptimal results of anaphora resolution, the system was extended with the second anaphora resolution module. The present paper also describes the first attempts of the internal document data representation.

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“…Coreference resolution is a central problem in Natural Language Processing with a broad range of applications such as summarization (Steinberger et al, 2007), textual entailment (Mirkin et al, 2010), information extraction (McCarthy and Lehnert, 1995), and dialogue systems (Strube and Müller, 2003). Traditionally, the resolution of noun phrases (NPs) has been the focus of coreference research .…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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Two uses of anaphora resolution in summarization

Cited by 121 publications

References 14 publications

Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

The role of statistical and semantic features in single-document extractive summarization

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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