Exploiting Rhetorical Relations in Blog Summarization

Mithun, Shamima

doi:10.1007/978-3-642-13059-5_53

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…To ensure that our results were not tailored to one specific summarizer, we used two different systems: BlogSum [24], an automatic summarizer based on discourse relations and MEAD [25], a generalized automatic summarization system. In order to generate syntactic trees for our experiment, we used the Stanford Parser [22].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Syntactic Sentence Compression for Text Summarisation

Perera

Kosseim

2013

Natural Language Processing and Information Systems

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Abstract. This paper presents our work on the evaluation of syntactic based sentence compression for automatic text summarization. Sentence compression techniques can contribute to text summarization by removing redundant and irrelevant information and allowing more space for more relevant content. However, very little work has focused on evaluating the contribution of this idea for summarization. In this paper, we focus on pruning individual sentences in extractive summaries using phrase structure grammar representations. We have implemented several syntaxbased pruning techniques and evaluated them in the context of automatic summarization, using standard evaluation metrics. We have performed our evaluation on the TAC and DUC corpora using the BlogSum and MEAD summarizers. The results show that sentence pruning can achieve compression rates as low as 60%, however when using this extra space to fill in more sentences, ROUGE scores do not improve significantly.

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Section: Discussionmentioning

confidence: 99%

Evaluating Syntactic Sentence Compression for Text Summarisation

Perera

Kosseim

2013

Natural Language Processing and Information Systems

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“…Furthermore, the mapping will be useful for researchers and engineers working on automatic coherence relation labeling. Many natural language processing tasks, such as information retrieval and question-answering systems (Bosma 2005;Verberne et al 2007), text summarization systems (Louis et al 2010;Marcu 2000;Mithun 2010), and machine translation systems (e.g., Koehn 2009;Meyer and Popescu-Belis 2012;Meyer et al 2011) would improve from increased performance in automated coherence relation classification. Current state-of-the-art coherence relation classification systems (see Xue et al 2015 for an overview) make use of human-annotated coherence relations in corpora for training, especially the large resources PDTB and RST-DT.…”

Section: Goal Of the Papermentioning

confidence: 99%

Unifying dimensions in coherence relations: How various annotation frameworks are related

Sanders

Demberg

Hoek

et al. 2018

Corpus Linguistics and Linguistic Theory

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In this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical newspaper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the proposed dimensions indeed create an interface that makes existing annotation systems “talk to each other.”

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“…Concerning approaches producing sentiment-based summaries initially focused mainly on blogs (Balahur-Dobrescu et al, 2009;Missen et al, 2009;Mithun, 2010); There is also a high number of approaches addressing the summarization of reviews (Zhuang et al, 2006;Lerman and McDonald, 2009;AleEbrahim and Fathian, 2013;Ravi Kumar and Raghuveer, 2013;Raut and Londhe, 2014), forums (Carbonaro, 2010;Ren et al, 2011) as well as microblogs (Sharifi et al, 2010;Harabagiu and Hickl, 2011;Chakrabarti and Punera, 2011;Bahrainian and Dengel, 2013). We also find Farzindar (2014), focusing on how summarization tasks can improve social media retrieval and event detection, and Li et al (2014), in which the multi-document summarization by sentence compression is explored.…”

Section: Related Workmentioning

confidence: 99%

A novel concept-level approach for ultra-concise opinion summarization

Lloret

Boldrini

Vodolazova

et al. 2015

Expert Systems with Applications

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The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users comunicate, exchange, and share opinions. The explotation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed.Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of stateof-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

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Exploiting Rhetorical Relations in Blog Summarization

Cited by 8 publications

References 34 publications

Evaluating Syntactic Sentence Compression for Text Summarisation

Evaluating Syntactic Sentence Compression for Text Summarisation

Unifying dimensions in coherence relations: How various annotation frameworks are related

A novel concept-level approach for ultra-concise opinion summarization

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