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WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization
In the Query Focused Multi-Document Summarization (QF-MDS) task, a set o...
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Abstractive Query Focused Summarization with Query-Free Resources
The availability of large-scale datasets has driven the development of n...
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Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
End-to-end neural models have made significant progress in question answ...
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Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
Query Focused Summarization (QFS) has been addressed mostly using extrac...
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A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
We consider the problem of using sentence compression techniques to faci...
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Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approache...
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Query Resolution for Conversational Search with Limited Supervision
In this work we focus on multi-turn passage retrieval as a crucial compo...
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Query Focused Multi-Document Summarization with Distant Supervision
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. In this work, we leverage distant supervision from question answering where various resources are available to more explicitly capture the relationship between queries and documents. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query, likely to contain an answer, and central. Under this framework, a trained evidence estimator further discerns which retrieved segments might answer the query for final selection in the summary. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.
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