A survey on evaluation of summarization methods

Ermakova, Liana; Cossu, Jean Valére; Mothe, Josiane

doi:10.1016/j.ipm.2019.04.001

Cited by 55 publications

(23 citation statements)

References 14 publications

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“…We will focus on this issue as well as feature selection in future work. We also would like to study additional components from the information nutritional label such as readability for which many measures exist [5] and could be reused.…”

Section: Discussionmentioning

confidence: 99%

“…We hypothesize that a sentence with a high emotional level might be used to deceive the "consumer" towards accepting false information and thus that it should be checked. We use the list of 2, 477 emotional words with evaluation from AFINN 5 [17], for example, abusive (-3), proud (2), etc. We develop three feature variants for the emotion criterion as follows:…”

Section: Emotionmentioning

confidence: 99%

See 1 more Smart Citation

Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Lespagnol

Mothe

Ullah

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Automatic fact-checking is an important challenge nowadays since anyone can write about anything and spread it in social media, no matter the information quality. In this paper, we revisit the information check-worthiness problem and propose a method that combines the "information nutritional label" features with POS-tags and word-embedding representations. To predict the information check-worthy claim, we train a machine learning model based on these features. We experiment and evaluate the proposed approach on the CheckThat! CLEF 2018 collection. The experimental result shows that our model that combines information nutritional label and word-embedding features outperforms the baselines and the official participants' runs of CheckThat! 2018 challenge.

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Section: Discussionmentioning

confidence: 99%

Section: Emotionmentioning

confidence: 99%

Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Lespagnol

Mothe

Ullah

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Later studies incorporate lexical, syntactic, and discourse-level features to predict text readability [33]. In NLP tasks, readability, coherence, conciseness, and grammar are usually assessed manually since it is difficult to express these parameters numerically [13]. However, several studies were carried out in the domain of automatic readability evaluation, including the application of language models [10,17,22,36] and machine learning techniques [17,32].…”

Section: Readabilitymentioning

confidence: 99%

Text Simplification for Scientific Information Access

Ermakova

Bellot

Braslavski

et al. 2021

Lecture Notes in Computer Science

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Modern information access systems hold the promise to give users direct access to key information from authoritative primary sources such as scientific literature, but non-experts tend to avoid these sources due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. The CLEF 2021 SimpleText track will address the opportunities and challenges of text simplification approaches to improve scientific information access head-on. We aim to provide appropriate data and benchmarks, starting with pilot tasks in 2021, and create a community of NLP and IR researchers working together to resolve one of the greatest challenges of today.

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“…Notwithstanding the most recent advancement found in text summarization, the issue has not yet been completely settled. The most significant data in the huge collection of text document is discovered by automated text summarization techniques and it able to consolidate and summarize the contents to be read by the users [2]. Also, it is important to acquire certain highlights by preparing information to abbreviate the time spent getting to data.…”

Section: Introductionmentioning

confidence: 99%

“…It has the goal to provide huge content in a packed and conceivable structure. The most known categories for the text summarization are as follows [2]:…”

Section: Introductionmentioning

confidence: 99%

Multi-Documents Extractive Text Summarization using Node Centrality

Kuriakose,

Umadevi

2020

IJEAT

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The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.

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A survey on evaluation of summarization methods

Cited by 55 publications

References 14 publications

Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Text Simplification for Scientific Information Access

Multi-Documents Extractive Text Summarization using Node Centrality

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