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The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. A key challenge in addressing this task is the lack of large labeled data for training the summarization model. In this article, we address this challenge by exploring a series of domain adaptation techniques. Given the recent success of pre-trained transformer models in a wide range of natural language processing tasks, we utilize such models to generate abstractive summaries for the QFTS task for both single-document and multi-document scenarios. For domain adaptation, we apply a variety of techniques using pre-trained transformer-based summarization models including transfer learning, weakly supervised learning, and distant supervision. Extensive experiments on six datasets show that our proposed approach is very effective in generating abstractive summaries for the QFTS task while setting a new state-of-the-art result in several datasets across a set of automatic and human evaluation metrics. [ABSTRACT FROM AUTHOR]

Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved. [ABSTRACT FROM PUBLISHER]

Abstract: Document ranking is an essential problem in the field of information retrieval (IR). Traditional weighting models such as BM25 and Language model can only take advantage of query terms. IR is a complex process that may be affected by a series of heterogeneous features. It is necessary to refine first-pass retrieval results by taking rich features into account. Traditional heuristic re-ranking approaches can only take advantage of a small number of homogeneous features that may affect information retrieval performance. In this paper, we propose and evaluate a regression-based document re-ranking approach for IR, in which we use SVM regression model to learn a re-ranking function automatically. Under this regression-based framework, we can take advantage of rich features to re-rank the firs-pass retrieved documents by traditional weighting models. We conduct a series of experiments on four standard IR collections in two different languages. The experimental results show that our proposed approach can significantly improve the retrieval performance over the first-pass retrieval. Moreover, by refining the first-pass retrieved document set, the traditional pseudo relevant feedback approaches can also be enhanced. [Copyright &y& Elsevier]