13,578 research outputs found
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
Concept maps can be used to concisely represent important information and
bring structure into large document collections. Therefore, we study a variant
of multi-document summarization that produces summaries in the form of concept
maps. However, suitable evaluation datasets for this task are currently
missing. To close this gap, we present a newly created corpus of concept maps
that summarize heterogeneous collections of web documents on educational
topics. It was created using a novel crowdsourcing approach that allows us to
efficiently determine important elements in large document collections. We
release the corpus along with a baseline system and proposed evaluation
protocol to enable further research on this variant of summarization.Comment: Published at EMNLP 201
A Supervised Approach to Extractive Summarisation of Scientific Papers
Automatic summarisation is a popular approach to reduce a document to its
main arguments. Recent research in the area has focused on neural approaches to
summarisation, which can be very data-hungry. However, few large datasets exist
and none for the traditionally popular domain of scientific publications, which
opens up challenging research avenues centered on encoding large, complex
documents. In this paper, we introduce a new dataset for summarisation of
computer science publications by exploiting a large resource of author provided
summaries and show straightforward ways of extending it further. We develop
models on the dataset making use of both neural sentence encoding and
traditionally used summarisation features and show that models which encode
sentences as well as their local and global context perform best, significantly
outperforming well-established baseline methods.Comment: 11 pages, 6 figure
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets
Abstract Meaning Representation for Multi-Document Summarization
Generating an abstract from a collection of documents is a desirable
capability for many real-world applications. However, abstractive approaches to
multi-document summarization have not been thoroughly investigated. This paper
studies the feasibility of using Abstract Meaning Representation (AMR), a
semantic representation of natural language grounded in linguistic theory, as a
form of content representation. Our approach condenses source documents to a
set of summary graphs following the AMR formalism. The summary graphs are then
transformed to a set of summary sentences in a surface realization step. The
framework is fully data-driven and flexible. Each component can be optimized
independently using small-scale, in-domain training data. We perform
experiments on benchmark summarization datasets and report promising results.
We also describe opportunities and challenges for advancing this line of
research.Comment: 13 page
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
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