Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-Focused Extractive Summarization

Roitman, Haggai; Feigenblat, Guy; Cohen, Doron; Boni, Odellia; Konopnicki, David

doi:10.1145/3366423.3380009

Cited by 17 publications

(19 citation statements)

References 17 publications

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“…These baselines generate the summaries of all documents in a document set which are then ranked using RoBERTa MS-MARCO . Moreover, we compare our model with four recent works: i) CES-50 (Feigenblat et al, 2017), ii) RSA (Baumel et al, 2018), iii) QUERYSUM (Xu and Lapata, 2020), and iv) DUAL-CES (Roitman et al, 2020 (Baumel et al, 2018), we find that our model outperforms them in all datasets in terms of both R-1 and R-2 Recall, but fails to outperform them in R-SU4 scores. Moreover, we find based on paired t-test (p ≤ .05) that the weakly supervised learning significantly outperforms the baselines in terms of both Recall and F1.…”

Section: Resultsmentioning

confidence: 74%

“…They adopted the Pointer Generation Network (PGN) (See et al, 2017) pre-trained for the generic abstractive summarization task in a large dataset to predict the query focused summaries in the target dataset via modifying the attention mechanism of the PGN model. However, their model failed to outperform different extractive approaches in terms of various ROUGE scores (Feigenblat et al, 2017;Roitman et al, 2020).…”

Section: Related Workmentioning

confidence: 98%

“…Early work on multi-document summarization was mostly focused on generic summarization (Nayeem et al, 2018), whereas the amount of work for QF-MDS had been very limited (Yao et al, 2017). Due to the lack of training data for the QF-MDS task, most previous works were based on various unsupervised approaches that could only generate extractive summaries (Wang et al, 2008;Wan and Xiao, 2009;Haghighi and Vanderwende, 2009;Wan and Zhang, 2014;Yao et al, 2015;Zhong et al, 2015;Ma et al, 2016;Feigenblat et al, 2017;Roitman et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

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WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Laskar¹,

Hoque²,

Huang³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multidocument gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.

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Section: Resultsmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Laskar¹,

Hoque²,

Huang³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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“…Therefore, search snippet generation can be considered as one kind of Query-focused Summarization (QFS). Similar to generic document summarization, this body of work can also be divided into extractive approaches (Zhu et al, 2019;Feigenblat et al, 2017;Roitman et al, 2020) and abstractive approaches (Laskar et al, 2020a;Baumel et al, 2018;Chen et al, 2020a;Su et al, 2020;Laskar et al, 2020b). As some PTMs are proved to be effective in text generation, most existing work adopted PTMs to generate abstractive snippets.…”

Section: Snippet Generationmentioning

confidence: 99%

Pre-training Methods in Information Retrieval

Fan¹,

Xie²,

Cai³

et al. 2021

Preprint

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The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to user's information need. Recently, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Since there have been a large number of works dedicating to the

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“…We take the two top central customer and agent sentences (2+2). Cross Entropy Summarizer (extractive)-CES is an unsupervised, extractive summarizer (Roitman et al, 2020;Feigenblat et al, 2017), which considers the summarization problem as a multi-criteria optimization over the sentences space, where several summary quality objectives are considered. The aim is to select a subset of sentences optimizing these quality objectives.…”

Section: Baselinesmentioning

confidence: 99%

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

Feigenblat

Gunasekara

Sznajder

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on realworld customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.

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Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-Focused Extractive Summarization

Cited by 17 publications

References 17 publications

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Pre-training Methods in Information Retrieval

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

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