The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

Society and individuals are negatively influenced both politically and socially by the
widespread increase of fake news either way generated by humans or machines. In the era …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Learning to rank has been intensively studied and widely applied in information retrieval.
Typically, a global ranking function is learned from a set of labeled data, which can achieve …

As an alternative to question answering methods based on feature engineering, deep
learning approaches such as convolutional neural networks (CNNs) and Long Short-Term …

Question Answering (QA) systems aim at supplying precise answers to questions, posed by
users in a natural language form. They are used in a wide range of application areas, from …

In learning-to-rank for information retrieval, a ranking model is automatically learned from
the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal …

Identifying the veracity of a news article is an interesting problem while automating this
process can be a challenging task. Detection of a news article as fake is still an open …

How to obtain an unbiased ranking model by learning to rank with biased user feedback is
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …

Considering the widespread use of mobile and voice search, answer passage retrieval for
non-factoid questions plays a critical role in modern information retrieval systems. Despite …