Analysis of Abstractive and Extractive Summarization Methods

This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PD

Effective summarisation for search engines

Users of information retrieval (IR) systems issue queries to find information in large collections of documents. Nearly all IR systems return answers in the form of a list of results, where each entry typically consists of the title of the underlying document, a link, and a short query-biased summary of a document's content called a snippet. As retrieval systems typically return a mixture of relevant and non-relevant answers, the role of the snippet is to guide users to identify those documents that are likely to be good answers and to ignore those that are less useful. This thesis focuses on techniques to improve the generation and evaluation of query-biased summaries for informational requests, where users typically need to inspect several documents to fulfil their information needs. We investigate the following issues: how users construct query-biased summaries, and how this compares with current automatic summarisation methods; how query expansion can be applied to sentence-level ranking to improve the quality of query-biased summaries; and, how to evaluate these summarisation approaches using sentence-level relevance data. First, through an eye tracking study, we investigate the way in which users select information from documents when they are asked to construct a query-biased summary in response to a given search request. Our analysis indicates that user behaviour differs from the assumptions of current state-of-the-art query-biased summarisation approaches. A major cause of difference resulted from vocabulary mismatch, a common IR problem. This thesis then examines query expansion techniques to improve the selection of candidate relevant sentences, and to reduce the vocabulary mismatch observed in the previous study. We employ a Cranfield-based methodology to quantitatively assess sentence ranking methods based on sentence-level relevance assessments available in the TREC Novelty track, in line with previous work. We study two aspects of sentence-level evaluation of this track. First, whether sentences that have been judged based on relevance, as in the TREC Novelty track, can also be considered to be indicative; that is, useful in terms of being part of a query-biased summary and guiding users to make correct document selections. By conducting a crowdsourcing experiment, we find that relevance and indicativeness agree around 73% of the time. Second, during our evaluations we discovered a bias that longer sentences were more likely to be judged as relevant. We then propose a novel evaluation of sentence ranking methods, which aims to isolate the sentence length bias. Using our enhanced evaluation method, we find that query expansion can effectively assist in the selection of short sentences. We conclude our investigation with a second study to examine the effectiveness of query expansion in query-biased summarisation methods to end users. Our results indicate that participants significantly tend to prefer query-biased summaries aided through expansion techniques approximately 60% of the time, for query-biased summaries comprised of short and middle length sentences. We suggest that our findings can inform the generation and display of query-biased summaries of IR systems such as search engines

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Hierarchical organization of consumer reviews for products and its applications

Ph.DDOCTOR OF PHILOSOPH

Implicit Entity Networks: A Versatile Document Model

The time in which we live is often referred to as the Information Age. However, it can also aptly be characterized as an age of constant information overload. Nowhere is this more present than on the Web, which serves as an endless source of news articles, blog posts, and social media messages. Of course, this overload is even greater in professions that handle the creation or extraction of information and knowledge, such as journalists, lawyers, researchers, clerks, or medical professionals. The volume of available documents and the interconnectedness of their contents are both a blessing and a curse for the contemporary information consumer. On the one hand, they provide near limitless information, but on the other hand, their consumption and comprehension requires an amount of time that many of us cannot spare. As a result, automated extraction, aggregation, and summarization techniques have risen in popularity, even though they are a long way from being comprehensive. When we, as humans, are faced with an overload of information, we tend to look for patterns that bring order into the chaos. In news, we might identify familiar political figures or celebrities, whereas we might look for expressive symptoms in medicine, or precedential cases in law. In other words, we look for known entities as reference points, and then explore the content along the lines of their relations to others entities. Unfortunately, this approach is not reflected in current document models, which do not provide a similar focus on entities. As a direct result, the retrieval of entity-centric knowledge and relations from a flood of textual information becomes more difficult than it has to be, and the inclusion of external knowledge sources is impeded. In this thesis, we introduce implicit entity networks as a comprehensive document model that addresses this shortcoming and provides a holistic representation of document collections and document streams. Based on the premise of modelling the cooccurrence relations between terms and entities as first-class citizens, we investigate how the resulting network structure facilitates efficient and effective entity-centric search, and demonstrate the extraction of complex entity relations, as well as their summarization. We show that the implicit network model is fully compatible with dynamic streams of documents. Furthermore, we introduce document aggregation methods that are sensitive to the context of entity mentions, and can be used to distinguish between different entity relations. Beyond the relations of individual entities, we introduce network topics as a novel and scalable method for the extraction of topics from collections and streams of documents. Finally, we combine the insights gained from these applications in a versatile hypergraph document model that bridges the gap between unstructured text and structured knowledge sources

Coherence in natural language : data structures and applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, February 2005.Includes bibliographical references (leaves [143]-148).(cont.) baseline, and that some coherence-based approaches best predict the human data. However, coherence-based algorithms that operate on trees did not perform as well as coherence-based algorithms that operate on more general graphs. It is suggested that that might in part be due to the fact that more general graphs are more descriptively adequate than trees for representing discourse coherence.The general topic of this thesis is coherence in natural language, where coherence refers to informational relations that hold between segments of a discourse. More specifically, this thesis aims to (1) develop criteria for a descriptively adequate data structure for representing discourse coherence; (2) test the influence of coherence on psycholinguistic processes, in particular, pronoun processing; (3) test the influence of coherence on the relative saliency of discourse segments in a text. In order to address the first aim, a method was developed for hand-annotating a database of naturally occurring texts for coherence structures. The thus obtained database of coherence structures was used to test assumptions about descriptively adequate data structures for representing discourse coherence. In particular, the assumption that discourse coherence can be represented in trees was tested, and results suggest that more powerful data structures than trees are needed (labeled chain graphs, where the labels represent types of coherence relations, and an ordered array of nodes represents the temporal order of discourse segments in a text). The second aim was addressed in an on-line comprehension and an off-line production experiment. Results from both experiments suggest that only a coherence-based account predicted the full range of observed data. In that account, the observed preferences in pronoun processing are not a result of pronoun-specific mechanisms, but a byproduct of more general cognitive mechanisms that operate when establishing coherence. In order to address the third aim, layout-, word-, and coherence-based approaches to discourse segment ranking were compared to human rankings. Results suggest that word-based accounts provide a strongby Florian Wolf.Ph.D