What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization

Zopf, Markus; Botschen, Teresa; Falke, Tobias; Beniamin, Heinzerling; Marasović, Ana; Mihaylov, Todor; P.V.S, Avinesh; Mencía, Eneldo Loza; Fürnkranz, Johannes; Frank, Anette

doi:10.1109/snams.2018.8554853

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Syntax representation of text can be applied to text summarization to leverage linguistic information because it assists in information filtering to obtain highlighted context from a source document [25], and yet the importance of this syntax has been previously underestimated [26]. When linguistic information is used to perform text summarization, it finds the relationships between terms in the document through sequence labeling (POS tagging [27], named entity recognition [28]), grammar analysis [29], and thesaurus usage (e.g., Wordnet) [30], and then extracts the salient context.…”

Section: B Syntax-aware Text Summarizationmentioning

confidence: 99%

Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Lee

Yang²,

Park

et al. 2021

IEEE Access

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Section: B Syntax-aware Text Summarizationmentioning

confidence: 99%

Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Lee

Yang²,

Park

et al. 2021

IEEE Access

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“…Syntax representation of text can be applied to text summarization to leverage linguistic information because it assists in information filtering to obtain highlighted context from a source document (Bouras and Tsogkas, 2008), and yet the importance of this syntax has been previously underestimated (Zopf et al, 2018). When linguistic information is used to perform text summarization, it finds the relationships between terms in the document through sequence labeling (POS tagging (Al-sharman and Pivkina, 2018), named entity recognition (Dobreva et al, 2020)), grammar analysis (Lu et al, 2019), and thesaurus usage (e.g., Wordnet) (Pal and Saha, 2014), and then extracts the salient context.…”

Section: A2 Syntax-aware Text Summarizationmentioning

confidence: 99%

Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning

Lee¹,

Yang²,

Park³

et al. 2021

Preprint

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ive dialogue summarization is a challenging task for several reasons. First, most of the important pieces of information in a conversation are scattered across utterances through multi-party interactions with different textual styles. Second, dialogues are often informal structures, wherein different individuals express personal perspectives, unlike text summarization, tasks that usually target formal documents such as news articles. To address these issues, we focused on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. Therefore, we constructed a syntax-aware model by leveraging linguistic information (i.e., POS tagging), which alleviates the above issues by inherently distinguishing sentences uttered from individual speakers. We employed multi-task learning of both syntax-aware information and dialogue summarization. To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task. Experiments on a SAMSum corpus (a large-scale dialogue summarization corpus) demonstrated that our method improved upon the vanilla model. We further analyze the costs and benefits of our approach relative to baseline models. * We define the speaker styles into Vitamin A,B, and C.

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State-of-the-art approach to extractive text summarization: a comprehensive review

Yadav

Ranvijay

Yadav

et al. 2023

Multimed Tools Appl

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What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization

Cited by 4 publications

References 17 publications

Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning

State-of-the-art approach to extractive text summarization: a comprehensive review

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