Source-linked AI summary
Multi-Topic Multi-Document Summarizer
Fatma El-Ghannam, Tarek El-Shishtawy
TL;DR
Multi-document summarization systems face limited coverage, inaccurate sentence selection, redundancy, and poor coherence. This paper introduces a concept-centroid approach with keyphrase-based Sen-Rich and Doc-Rich extraction; Sen-Rich led ROUGE-S against TAC 2011 systems, while human evaluation favored Doc-Rich for multi-topic documents.
Problem
Existing multi-document summarization systems have limitations including low coverage, inaccurate extraction of important sentences, redundancy, and poor coherence.
Method
The paper uses a concept-centroid approach in which keyphrases weight sentences and documents, with Sen-Rich favoring rich sentences and Doc-Rich favoring sentences from a centroid document.
Results
Sen-Rich outperformed all systems in ROUGE-S, while human evaluation of multi-topic documents showed Doc-Rich superior to Sen-Rich.
Takeaways & Limitations
Sen-Rich is useful for highly condensed summaries of closely related or single-event documents, whereas Doc-Rich is superior for multi-document, multi-topic summarization.
Takeaways & Limitations
Sen-Rich can lack coherence and sentence diversity because selected sentences may come from different documents and repeat dominant topics.
Abstract
from arXiv · showhide
Current multi-document summarization systems can successfully extract summary sentences, however with many limitations including: low coverage, inaccurate extraction to important sentences, redundancy and poor coherence among the selected sentences. The present study introduces a new concept of centroid approach and reports new techniques for extracting summary sentences for multi-document. In both techniques keyphrases are used to weigh sentences and documents. The first summarization technique (Sen-Rich) prefers maximum richness sentences. While the second (Doc-Rich), prefers sentences from centroid document. To demonstrate the new summarization system application to extract summaries of Arabic documents we performed two experiments. First, we applied Rouge measure to compare the new techniques among systems presented at TAC2011. The results show that Sen-Rich outperformed all systems in ROUGE-S. Second, the system was applied to summarize multi-topic documents. Using human evaluators, the results show that Doc-Rich is the superior, where summary sentences characterized by extra coverage and more cohesion.
1. INTRODUCTION
The paper addresses multi-document summarization, where multiple sources increase redundancy and make coherent sentence ordering difficult. It proposes a concept-centroid approach that automatically evaluates topic richness and balances topic importance with coverage.
- Multi-document summarization must manage greater redundancy and non-trivial ordering across information extracted from multiple sources.
- Extractive summarization selects salient textual units, whereas abstractive summarization additionally requires information fusion, compression, and reformulation.
- Existing successful systems often target news documents assumed to describe a single event or mono-concept.
- The proposed concept-centroid approach summarizes multi-topic document collections without requiring users to specify all topic aspects.
- The system automatically extracts domain-independent topics, ranks documents and sentences by topic richness, and balances concept importance with coverage.
2. PREVIOUS WORKS
Previous multi-document summarization work uses similarity, query relevance, clustering, lexical chains, and multilingual representations. The paper identifies coverage, coherence, and redundancy as central extraction and ordering concerns motivating its keyphrase-based alternative.
- Coverage: Coverage requires selecting salient information that represents different topics in the source documents.
- Coherency: Coherence remains difficult because sentences extracted from multiple documents must be ordered into a coherent context sequence.
- Redundancy elimination: Redundancy elimination selects one sentence among multiple sentences expressing the same information, often using similarity measures.
- Existing systems include centroid-based news summarization, query-based Arabic summarization, multilingual vector-centroid ranking, and lexical-chain extraction.
- Similarity-heavy multilingual methods often treat summarization as a bag of words while ignoring lexical and syntactic dependencies in extracted summaries.
3. THE PROPOSED SYSTEM
The proposed system extracts local keyphrases, constructs shared cluster topics, assesses document and sentence richness, and then uses distinct extraction strategies for Sen-Rich and Doc-Rich. Keyphrase extraction quality is a stated dependency of the summarizer output.
- The algorithm extracts local keyphrases, constructs cluster topics, assesses sentences and documents, and extracts important sentences for the summary.
- Local keyphrases receive document-level scores, then global cluster-topic scores based on local importance and relevance across the document cluster.
- Sentences are assessed according to their richness in important cluster topics before the two techniques apply different sentence-extraction approaches.
- Both word-representation granularity and extracted morpho-syntactic features directly affect the keyphrase extractor and therefore the summarizer output.
3.1 Local document keyphrase extractor
The local extractor processes each Arabic document at lemma level and generates scored keyphrases using linguistic and statistical features. These normalized keyphrase scores replace each document's raw text features for subsequent assessment.
- Each document is passed to LBAKE to extract indicative keyphrases at lemma level through linguistic processing, candidate phrase extraction, and feature calculation.
- LBAKE generates one-, two-, and three-word candidate phrases, retaining those that satisfy syntactic rules and representing words in lemma form.
- Keyphrase importance is based on nine features, including phrase length, frequency, location, verb content, and whether the sentence is a question.
- The extractor outputs scored keyphrases normalized to the document maximum, and each document is represented by these features.
3.2. Constructing cluster topics
The system constructs global cluster topics from local keyphrases by combining coverage, frequency, relevance, and document-link information. It then ranks centroid documents to emphasize topics relevant across the document cluster.
- Cluster topics are formed by combining local keyphrases from all documents and assigning global scores based on local importance and cross-document relevance.
- Top-ranked non-duplicated keyphrases preserve important local keyphrases in the global topic set and tend to maximize summary coverage.
- Keyphrase frequency alone is insufficient because repeated phrases may not accurately represent importance, motivating a centroid topic score.
- Centroid document scoring evaluates a document's relevance through links formed by matched keyphrases with other documents in the cluster.
- The proposed system can reject documents dominated by topics unrelated to the main cluster concepts.
3.3. Extraction of MD summary
The extraction stage ranks topics, documents, and sentences before ordering selected sentences. Sen-Rich favors topic-rich sentences, whereas the centroid-document technique favors coherent sentences from central documents.
- The system first ranks cluster sentences by salient features and then presents the selected sentences in an ordered sequence.
- Sentence richness technique (Sen-Rich): Sen-Rich scores each sentence by summing the scores of all cluster topics it contains, then selects the top n sentences for the target summary length.
- Sentence richness technique (Sen-Rich): Cluster topic scores combine document relevance and local topic coverage, allowing sentence selection to capture shared and document-specific concepts.
- Sentence richness technique (Sen-Rich): Sen-Rich is useful for highly condensed summaries but may lack coherence, guarantee insufficient diversity, and repeat dominant topics across sentences.
- Unit Overlap similarity thresholds can reduce similar sentences that describe the same subject or explain important aspects in different ways.
- Centroid document technique: The centroid-document technique prefers sentences from central documents when multiple sentences carry the same topic, producing a summary that follows their contextual flow.
4. EVALUATION OF THE PROPOSED TECHNIQUES
The evaluation used automatic ROUGE comparisons against TAC 2011 systems and human assessment on multi-topic documents. Sen-Rich led on ROUGE-S, while Doc-Rich performed better for coverage and cohesion in multi-topic summaries.
- Evaluation design: Two experiments evaluated the techniques using automatic comparison with TAC 2011 systems and human assessment of summaries for multi-topic documents.The TAC 2011 experiment produced 240–250-word summaries and used reference human summaries; the second used three human evaluators.
- Experiment 1: TAC 2011 comparison: ROUGE-S reached 0.2014 for Sen-Rich, compared with 0.1585 for Classy and 0.1554 for Doc-Rich.Sen-Rich outperformed all compared systems on this measure.
- Experiment 1: TAC 2011 comparison: Sen-Rich scores sentences by summing the scores of important cluster topics they contain, favoring sentences that cover many topics.This scoring scheme can capture a small number of sentences carrying important topics in clusters with limited topics.
- Experiment 2: Multi-topic documents: Doc-Rich averaged 11 versus 9.2 for Sen-Rich in human evaluations across four multi-topic clusters.The evaluation used three human scores, H1:H3, for clusters C1:C4.
- Experiment 2: Multi-topic documents: Doc-Rich extracted at most one sentence per important keyphrase, helping expose more important concepts and increase topic coverage.Its summaries also drew an average of 72% of sentences from the centroid document, whose ordering supported cohesion.
5. CONCLUSIONS
The study presents two keyphrase-based, concept-centroid techniques for multi-document summarization and evaluates them in two experiments. Sen-Rich performs best on ROUGE-S for TAC 2011 Arabic document clusters, while Doc-Rich is superior for multi-topic documents because of better coverage and cohesion.
- Contributions: The study introduces Sen-Rich and Doc-Rich as two keyphrase-based techniques built around a concept-centroid approach.Sen-Rich sums cluster-topic scores for sentences, whereas Doc-Rich identifies an important centroid document and extracts one sentence per important cluster topic.
- Techniques: Sen-Rich determines sentence importance by summing the scores of cluster topics represented in each sentence.
- Techniques: Doc-Rich ranks documents by importance, selects the most important as the centroid document, and extracts one sentence for each important cluster topic.
- Evaluation: Sen-Rich outperformed all compared systems in ROUGE-S on TAC 2011 Arabic document clusters.
- Evaluation: Human evaluation found Doc-Rich superior to Sen-Rich for multi-topic documents, producing summaries with better coverage and cohesive readability.
Authors
The authors are researchers whose work focuses on Arabic language processing, computational linguistics, natural language processing, and related computational projects.
- Fatma El-Ghannam: Fatma El-Ghannam is a Researcher Assistance at the Electronics Research Institute in Cairo, Egypt.
- Fatma El-Ghannam: Fatma El-Ghannam researches Arabic language generation and analysis and is preparing for a Ph.D. in NLP.
- Tarek El-Shishtawy: Tarek El-Shishtawy is a professor at the Faculty of Computers and Information at Benha University in Egypt.
- Tarek El-Shishtawy: Tarek El-Shishtawy has participated in Arabic computational linguistics projects and worked on a large-scale Arabic annotated corpus.
- Tarek El-Shishtawy: Tarek El-Shishtawy publishes on Arabic corpora, machine translation, text processing, and data mining.