To summarization of one or more document aims to create a strong summary while retaining the main characteristics of the original set of documents. To cover a number of topic with each theme represented by a cluster of highly related sentences. Sentence clustering is used, it directly generates clusters integrated with ranking. Ranking distribution for sentence in each and every cluster is diff...

Single-document summarization and multidocument summarization are very closely related tasks and they have been widely investigated independently. This paper examines the mutual influences between the two tasks and proposes a novel unified approach to simultaneous single-document and multidocument summarizations. The mutual influences between the two tasks are incorporated into a graph model an...

With the continuous attention of modern search engines to retrieve entities and not just documents for any given query, we introduce a new method for enhancing the entity-ranking task. An entity-ranking task is concerned with retrieving a ranked list of entities as a response to a specific query. Some successful models used the idea of association discovery in a window of text, rather than in t...

Learning effective feature-based ranking functions is a fundamental task for search engines, and has recently become an active area of research [10, 3, 2]. Many of these recent algorithms are based on the pairwise preference framework, in which instead of taking documents in isolation, document pairs are used as instances in the learning process. One disadvantage of this process is that a noisy...

Learning to rank is the emerging research field in many data mining applications and information retrieval techniques (e.g. Search engines). The major issue in ranking algorithm is that the quality or ranking is affected by labeled examples, since it is very expensive and also time consuming to collect labeled samples. This problem brings a great need for active learning algorithm; however, in ...

Protection of content of sensitive text documents is important in enterprise intranets. An index structure is needed to support efficient search and retrieval, but it can lead to information leakage; by statistical attacks an adversary can draw probabilistic inference about the contents of document collection. Zerr and others present a confidential index structure and the ranking of retrieved d...

In this paper, we detail our participation in two subtasks: subtopic mining and document ranking of the NTCIR-11 IMine task. In the subtopic mining subtask, to discover the latent hierarchy among query-like strings, our key idea is to structurally parse query-like strings by characterizing pairwise dependency in the bag-of-units perspective. Then the clustering algorithm (i.e., affinity propaga...

Extractive multi-document summarization is mostly treated as a sentence ranking problem. Existing graph-based ranking methods for key-sentence extraction usually attempt to compute a global importance score for each sentence under a single relation. Motivated by the fact that both documents and sentences can be presented by a mixture of semantic topics detected by Latent Dirichlet Allocation (L...

Search engines split large datasets across multiple machines using document distribution. Documents are typically distributed randomly to produce good load balancing. We propose that documents be distributed by their size instead. This can make load balancing more difficult, but it produces immediate improvements in both index size and query throughput. To support our proposal, we show improvem...

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 document. Given a sentence set from both t...