We propose a novel abstractive summarization system for product reviews by taking advantage of their discourse structure.First, we apply a discourse parser to each review and obtain a discourse tree representation for every review.We then modify the discourse trees such that every leaf node only contains the aspect words.Second, we aggregate the aspect discourse trees and generate a graph.We then select a subgraph representing the most important aspects and the rhetorical relations between them using a PageRank algorithm, and transform the selected subgraph into an aspect tree.Finally, we generate a natural language summary by applying a template-based NLG framework.Quantitative and qualitative analysis of the results, based on two user studies, show that our approach significantly outperforms extractive and abstractive baselines.

Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation.

The enormous amount of user-generated data available on the Web provides a great opportunity to understand, analyze, and exploit people’s opinions on different topics. Traditional Information Retrieval methods consider the relevance of documents to a topic but are unable to differentiate between subjective and objective documents. Opinion retrieval is a retrieval task in which not only the relevance of a document to the topic is important but also the amount of opinion expressed in the document about the topic. In this article, we address the blog post opinion retrieval task and propose methods that rank blog posts according to their relevance and opinionatedness toward a topic. We propose estimating the opinion density at each position in a document using a general opinion lexicon and kernel density functions. We propose and investigate different models for aggregating the opinion density at query terms positions to estimate the opinion score of every document. We then combine the opinion score with the relevance score based on a probabilistic justification. Experimental results on the BLOG06 dataset show that the proposed method provides significant improvement over the standard TREC baselines. The proposed models also achieve much higher performance compared to all state of the art methods.

Blog post opinion retrieval is the problem of ranking blog posts according to the likelihood that the post is relevant to the query and that the author was expressing an opinion about the topic (of the query). A recent study has proposed a method for finding the opinion density at query term positions in a document which uses the proximity of query term and opinion term as an indicator of their relatedness. The maximum opinion density between different query positions was used as an opinion score of the whole document. In this paper we investigate the effect of exploiting multiple opinion evidence of a document. We propose using the ordered weighted averaging (OWA) operator in order to combine the opinion score of different query positions for a final score of a document, in the proximity-based opinion retrieval system.

This paper addresses blog feed retrieval where the goal is to retrieve the most relevant blog feeds for a given user query. Since the retrieval unit is a blog, as a collection of posts, performing relevance feedback techniques and selecting the most appropriate documents for query expansion becomes challenging. By assuming time as an effective parameter on the blog posts content, we propose a time-based query expansion method. In this method, we select terms for expansion using most relevant days for the query, as opposed to most relevant documents. This provide us with more trustable terms for expansion. Our preliminary experiments on Blog08 collection shows that this method can outperform state of the art relevance feedback methods in blog retrieval.

Blog post opinion retrieval aims at finding blog posts that are relevant and opinionated about a user's query. In this paper we propose a simple probabilistic model for assigning relevant opinion scores to documents. The key problem is how to capture opinion expressions in the document, that are related to the query topic. Current solutions enrich general opinion lexicons by finding query-specific opinion lexicons using pseudo-relevance feedback on external corpora or the collection itself. In this paper we use a general opinion lexicon and propose using proximity information in order to capture opinion term relatedness to the query. We propose a proximity-based opinion propagation method to calculate the opinion density at each point in a document. The opinion density at the position of a query term in the document can then be considered as the probability of opinion about the query term at that position. The effect of different kernels for capturing the proximity is also discussed. Experimental results on the BLOG06 dataset show that the proposed method provides significant improvement over standard TREC baselines and achieves a 2.5% increase in MAP over the best performing run in the TREC 2008 blog track.