most citedA Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

99 citations · 147 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CL20161 cited

Leveraging Semantic Web Search and Browse Sessions for Multi-Turn Spoken Dialog Systems

Lu Wang, Larry Heck, Dilek Hakkani-Tur

Training statistical dialog models in spoken dialog systems (SDS) requires large amounts of annotated data. The lack of scalable methods for data mining and annotation poses a sign…

cs.CL201611 cited

Summarizing Decisions in Spoken Meetings

Lu Wang, Claire Cardie

This paper addresses the problem of summarizing decisions in spoken meetings: our goal is to produce a concise {\it decision abstract} for each meeting decision. We explore and com…

cs.CL201628 cited

Focused Meeting Summarization via Unsupervised Relation Extraction

Lu Wang, Claire Cardie

We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction. We adapt an existing in-domain relation le…

cs.CL20168 cited

Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings

Lu Wang, Claire Cardie

We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words. Concretely, a series of unsupe…

cs.CL201699 cited

A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

Lu Wang, Hema Raghavan, Vittorio Castelli +2

We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. We present a sentence-compression-based framework for the…

cs.CL2016

Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon

Lu Wang, Claire Cardie

We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make…