Lifu Huang

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Recently, much research focuses on event s-toryline generation, which aims to produce a concise, global and temporal event summary from a collection of articles. Generally, each event contains multiple sub-events and the sto-ryline should be composed by the component summaries of all the sub-events. However, different sub-events have different part-whole(More)
Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity(More)
Event detection remains a challenge due to the difficulty at encoding the word semantics in various contexts. Previous approaches heavily depend on language-specific knowledge and pre-existing natural language processing (NLP) tools. However, compared to English, not all languages have such resources and tools available. A more promising approach is to(More)
We propose a brand new " Liberal " Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Representation) and distributional semantics to detect and represent event structures and adopt a joint typing framework to simultaneously extract event types and(More)
In this paper we study how to summarize travel-related information in forum threads to generate supplementary travel guides. Such summaries presumably can provide additional and more up-to-date information to tourists. Existing multi-document summarization methods have limitations for this task because (1) they do not generate structured summaries but(More)
With the popularity of Web 2.0, comments left by readers on web documents have drawn much attention. In this paper, we study the problem of comments-oriented document summarization, which aims to summarize a web document by considering not only its content but also the comments. Generally, most of the comments usually convey one or a few aspects of the(More)
Most existing learning to rank based summarization methods only used content relevance of sentences with respect to queries to rank or estimate sentences, while neglecting sentence relationships. In our work, we propose a novel model, RelationListwise, by integrating relation information among all the estimated sentences into listMLE-Top K, a basic listwise(More)
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