Supervised Learning of Automatic Pyramid for Optimization-Based
            Multi-Document Summarization

Peyrard, Maxime; Eckle‐Kohler, Judith

doi:10.18653/v1/p17-1100

Cited by 18 publications

(12 citation statements)

References 14 publications

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“…A summarizer intends to extract or generate a summary maximizing θ I . This fits within the general optimization framework for summarization (McDonald, 2007;Peyrard and Eckle-Kohler, 2017b;Peyrard and Gurevych, 2018) The background knowledge and the choice of semantic units are free parameters of the theory. They are design choices which can be explored empirically by subsequent works.…”

Section: Discussionmentioning

confidence: 99%

A Simple Theoretical Model of Importance for Summarization

Peyrard¹

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this end, we propose simple but rigorous definitions of several concepts that were previously used only intuitively in summarization: Redundancy, Relevance, and Informativeness. Importance arises as a single quantity naturally unifying these concepts. Additionally, we provide intuitions to interpret the proposed quantities and experiments to demonstrate the potential of the framework to inform and guide subsequent works.

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Section: Discussionmentioning

confidence: 99%

A Simple Theoretical Model of Importance for Summarization

Peyrard¹

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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“…One important architecture is to model MDS as a budgeted maximum coverage problem, including the prior approach (Mc-Donald, 2007) and improved models (Woodsend and Lapata, 2012;Li et al, 2013;Boudin et al, 2015). There are still recent studies under traditional extractive framework (Peyrard and Eckle-Kohler, 2017;Avinesh and Meyer, 2017).…”

Section: Extractive Summarization Methodsmentioning

confidence: 99%

Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study

Zhang¹,

Tan²,

Wan³

2018

Proceedings of the 11th International Conference on Natural Language Generation

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Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.

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“…For example, the frequency computation of words or n-grams can be replaced with learned weights Li et al 2013). Additionally, structured output learning permits to score smaller units while providing supervision at the summary level (Li et al 2009;Peyrard and Eckle-Kohler 2017).…”

Section: Extractive Summarizationmentioning

confidence: 99%

Live blog summarization

Avinesh

Peyrard

Meyer

2021

Lang Resources & Evaluation

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Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give their readers a minute by minute update on an event. Good summaries enhance the value of the live blogs for a reader, but are often not available. In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. We show that live blog summarization poses new challenges in the field of news summarization, since frequency and positional signals cannot be used. We make our tools publicly available to reconstruct the corpus and to conduct our empirical experiments. This encourages the research community to build upon and replicate our results.

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Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization

Cited by 18 publications

References 14 publications

A Simple Theoretical Model of Importance for Summarization

A Simple Theoretical Model of Importance for Summarization

Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study

Live blog summarization

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