Statistical Summarization at QA@INEX 2011 Track Using Cortex and Enertex Systems

Torres‐Moreno, Juan‐Manuel; Velázquez-Morales, Patricia; Gagnon, Michel

doi:10.1007/978-3-642-35734-3_23

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…• In REG-run, participants [21] used an automatic greedy summarizer named REG (REsumeur Glouton) which uses graph methods to spot the most important sentences in the document.…”

Section: Comparisonmentioning

confidence: 99%

Using Semantic Query Expansion and Relevance Feedback for an Effective Tweet Contextualization Process

Dhokar

Hlaoua

Romdhane

2023

Preprint

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Bound to 240 characters, tweets are short and ambiguous by nature due to the way they are written, without maintaining formal grammar and proper spelling. These constraints increase the possibility of the tweet’s misunderstanding. Thus, it is essential to know the original context of their realization. This paper falls under the tweet contex-tualization task which aims to produce an informative and coherent paragraph, called a context, from a set of documents in response to topics treated by the tweet, allowing a reader to better understand the tweet. We propose to combine a semantic query expansion approach and Relevance Feedback technique in order to enhance queries (tweets) and documents (returned by the Information Retrieval System), to produce more informative contexts.The effectiveness of our method is proved through an experimental study conducted on the INEX 2014 collection.

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“…• In REG-run, participants [21] used an automatic greedy summarizer named REG (REsumeur Glouton) which uses graph methods to spot the most important sentences in the document.…”

Section: Comparisonmentioning

confidence: 99%

Using Semantic Query Expansion and Relevance Feedback for an Effective Tweet Contextualization Process

Dhokar

Hlaoua

Romdhane

2023

Preprint

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“…Cortex combines such metrics as word frequency, overlap with query terms, entropy of the words, shape of text etc. [11]. In Enertex sentence score is calculated from text energy matrix [11].…”

Section: Introductionmentioning

confidence: 99%

“…[11]. In Enertex sentence score is calculated from text energy matrix [11]. REG is an enhancement of Cortex which uses query expansion (QE) [12].…”

Section: Introductionmentioning

confidence: 99%

A Method for Short Message Contextualization: Experiments at CLEF/INEX

Ermakova

2015

Lecture Notes in Computer Science

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Abstract. This paper presents the approach we developed for automatic multi-document summarization applied to short message contextualization, in particular to tweet contextualization. The proposed method is based on named entity recognition, part-of-speech weighting and sentence quality measuring. In contrast to previous research, we introduced an algorithm from smoothing from the local context. Our approach exploits topic-comment structure of a text. Moreover, we developed a graph-based algorithm for sentence reordering. The method has been evaluated at INEX/CLEF tweet contextualization track. We provide the evaluation results over the 4 years of the track. The method was also adapted to snippet retrieval and query expansion. The evaluation results indicate good performance of the approach.

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Statistical Summarization at QA@INEX 2011 Track Using Cortex and Enertex Systems

Cited by 2 publications

References 4 publications

Using Semantic Query Expansion and Relevance Feedback for an Effective Tweet Contextualization Process

Using Semantic Query Expansion and Relevance Feedback for an Effective Tweet Contextualization Process

A Method for Short Message Contextualization: Experiments at CLEF/INEX

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