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: PDF

Design and Development of a Framework for an Automatic Answer Evaluation System Based on Similarity Measures

The assessment of answers is an important process that requires great effort from evaluators. This assessment process requires high concentration without any fluctuations in mood. This substantiates the need to automate answer script evaluation. Regarding text answer evaluation, sentence similarity measures have been widely used to compare student written answers with reference texts. In this paper, we propose an automated answer evaluation system that uses our proposed cosine-based sentence similarity measures to evaluate the answers. Cosine measures have proved to be effective in comparing between free text student answers and reference texts. Here we propose a set of novel cosine-based sentence similarity measures with varied approaches of creating document vector space. In addition to this, we propose a novel synset-based word similarity measure for computation of document vectors coupled with varied approaches for dimensionality-reduction for reducing vector space dimensions. Thus, we propose 21 cosine-based sentence similarity measures and measured their performance using MSR paraphrase corpus and Li’s benchmark datasets. We also use these measures for automatic answer evaluation system and compare their performances using the Kaggle short answer and essay dataset. The performance of the system-generated scores is compared with the human scores using Pearson correlation. The results show that system and human scores have correlation between each other.