A Modular Tool for Automatic Summarization

Nyzam, Valentin; Bossard, Aurélien

doi:10.18653/v1/p19-3030

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…It is a graph-based method that considers summarization as the extraction of the most central sentences in a graph. The implementation used is the one of Nyzam and Bossard (2019), freely available online 2 . Even if TextRank method was designed in 2004, it was shown in (Zheng and Lapata, 2019) that it still compares to more recent methods when there is no correlation between sentence position and centrality.…”

Section: Methodsmentioning

confidence: 99%

Neural Network-Based Generation of Sport Summaries: a Preliminary Study

Belemkoabga¹,

Bossard

Essa³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing M

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This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.

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Section: Methodsmentioning

confidence: 99%

Neural Network-Based Generation of Sport Summaries: a Preliminary Study

Belemkoabga¹,

Bossard

Essa³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing M

Self Cite

View full text Add to dashboard Cite

show abstract

“…For conditional generation, many demonstrations are summarization systems. For instance, Nyzam and Bossard (2019) present a modular tool for automatic summarization. showcase a visualization tool for summaries obtained by different summarization methods.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

2023

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Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, lowresource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system 1 .

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“…For conditional generation, many demonstrations are summarization systems. For instance, Nyzam and Bossard (2019) present a modular tool for automatic summarization. Syed et al (2021) showcase a visualization tool for summaries obtained by different summarization methods.…”

Section: Related Workmentioning

confidence: 99%

Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

Huot,

Maynez,

Narayan

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the blueprint in order to improve or control the generated output.A short video demonstrating our system is available at https://goo.gle/ text-blueprint-demo

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A Modular Tool for Automatic Summarization

Cited by 4 publications

References 19 publications

Neural Network-Based Generation of Sport Summaries: a Preliminary Study

Neural Network-Based Generation of Sport Summaries: a Preliminary Study

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

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