102 research outputs found
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Query-focused Summarization (QfS) deals with systems that generate summaries
from document(s) based on a query. Motivated by the insight that Reinforcement
Learning (RL) provides a generalization to Supervised Learning (SL) for Natural
Language Generation, and thereby performs better (empirically) than SL, we use
an RL-based approach for this task of QfS. Additionally, we also resolve the
conflict of employing RL in Transformers with Teacher Forcing. We develop
multiple Policy Gradient networks, trained on various reward signals: ROUGE,
BLEU, and Semantic Similarity, which lead to a 10-point improvement over the
State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5).
We also show performance of our approach in zero-shot setting for another
benchmark dataset (DebatePedia) -- our approach leads to results comparable to
baselines, which were specifically trained on DebatePedia. To aid the RL
training, we propose a better semantic similarity reward, enabled by a novel
Passage Embedding scheme developed using Cluster Hypothesis. Lastly, we
contribute a gold-standard test dataset to further research in QfS and
Long-form Question Answering (LfQA)
Question-driven text summarization with extractive-abstractive frameworks
Automatic Text Summarisation (ATS) is becoming increasingly important due to the exponential growth of textual content on the Internet. The primary goal of an ATS system is to generate a condensed version of the key aspects in the input document while minimizing redundancy. ATS approaches are extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) and then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate form and then constructs the summary using different sentences than the originals. The hybrid approach combines both the extractive and abstractive approaches. The query-based ATS selects the information that is most relevant to the initial search query. Question-driven ATS is a technique to produce concise and informative answers to specific questions using a document collection.
In this thesis, a novel hybrid framework is proposed for question-driven ATS taking advantage of extractive and abstractive summarisation mechanisms. The framework consists of complementary modules that work together to generate an effective summary: (1) discovering appropriate non-redundant sentences as plausible answers using a multi-hop question answering system based on a Convolutional Neural Network (CNN), multi-head attention mechanism and reasoning process; and (2) a novel paraphrasing Generative Adversarial Network (GAN) model based on transformers rewrites the extracted sentences in an abstractive setup. In addition, a fusing mechanism is proposed for compressing the sentence pairs selected by a next sentence prediction model in the paraphrased summary. Extensive experiments on various datasets are performed, and the results show the model can outperform many question-driven and query-based baseline methods. The proposed model is adaptable to generate summaries for the questions in the closed domain and open domain. An online summariser demo is designed based on the proposed model for the industry use to process the technical text
Neural Natural Language Processing for Long Texts: A Survey of the State-of-the-Art
The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long
document analysis are quite different from those of shorter texts, while the
ever increasing size of documents uploaded on-line renders automated
understanding of long texts a critical area of research. This article has two
goals: a) it overviews the relevant neural building blocks, thus serving as a
short tutorial, and b) it surveys the state-of-the-art in long document NLP,
mainly focusing on two central tasks: document classification and document
summarization. Sentiment analysis for long texts is also covered, since it is
typically treated as a particular case of document classification.
Additionally, this article discusses the main challenges, issues and current
solutions related to long document NLP. Finally, the relevant, publicly
available, annotated datasets are presented, in order to facilitate further
research.Comment: 53 pages, 2 figures, 171 citation
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
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